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16Understand how Claude Desktop and Claude Code tool search filtering works</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-calculate-llm-vram-and-tps-at-scale">How to Calculate LLM VRAM and TPS at Scale</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Aug 17, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">The exact formulas and techniques to calculate VRAM requirements and predict throughput for massive language models, avoiding costly out-of-memory errors during deployment.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/fix-chatgpt-connector-action-discovery-error">How to Fix ChatGPT Connector Action Discovery Errors</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Aug 7, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">olve ChatGPT action discovery probe errors by handling POST discovery probes with valid JSON-RPC fallback responses</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/the-ai-engagement-index-countries-leading-the-ai">The AI Engagement Index: Countries Leading the AI Adoption in 2026</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Jun 15, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">The updated AI Engagement Index ranks nations by their engagement in technical AI content, offering a fresh perspective on the global AI adoption.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/essential-machine-learning-models-guide">The Essential Machine Learning Models You Should Know</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Aditya S.<!-- --> <!-- -->on<!-- --> <!-- -->May 28, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">
16The fundamental algorithms of machine learning to build efficient, scalable, and highly optimized technical solutions.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-set-up-oauth-for-the-claude-connector">How to Set Up OAuth for the Claude Connector</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->May 26, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Step-by-step guide to walk you through the exact endpoints and response specs Claude expects, including the OIDC location and token exchange.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/effective-llm-coding-agent-instructions">Guide For Writing Effective LLM Coding Agent Instructions</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Apr 6, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Learn the essential techniques for optimizing your AI coding assistant. Discover how to write precise, minimal instructions to prevent context bloat and improve output accuracy for your software projects.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/why-no-vector-db-needed">5 Reasons Your AI App Doesn't Need A Vector Database</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Aaron T.<!-- --> <!-- -->on<!-- --> <!-- -->Apr 2, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Discover why a simple stack is superior to dedicated vector databases for 95% of AI applications, saving you costs and engineering overhead.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-get-started-with-openclaw">How to Get Started with OpenClaw using Docker</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Jack N.<!-- --> <!-- -->on<!-- --> <!-- -->Mar 26, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Learn how to deploy, secure, and automate your first local AI agent using OpenClaw and Docker.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/have-we-already-reached-agi">7 Reasons We Have Already Achieved AGI and Why the Goalpost Keeps Moving</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Rahul C.<!-- --> <!-- -->on<!-- --> <!-- -->Mar 24, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Understand why modern AI models already meet historical definitions of General Intelligence and how our shifting expectations create a "moving goalpost" for AGI.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-set-up-github-copilot-cli-with-your-mcp-ser">How to Set Up GitHub Copilot CLI with Your MCP Server</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Mar 23, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Steps to connect the GitHub Copilot CLI to a local Model Context Protocol (MCP) server. Augment your command-line workflow with custom developer tools, even on the free plan.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/qwen-3-5-system-requirement-vram-guide">GPU System Requirement Guide for Qwen 3.5</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Ryan A.<!-- --> <!-- -->on<!-- --> <!-- -->Mar 11, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Determine exactly how much VRAM you need to run Qwen 3.5 locally. We break down memory requirements for FP16 and Q4 quantization across all sizes, from 0.8B to the massive 397B-A17B model.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/best-local-llms-apple-silicon-mac">Best Local LLMs to Run On Every Apple Silicon Mac in 2026</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Ryan A.<!-- --> <!-- -->on<!-- --> <!-- -->Feb 1, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Top-performing local LLMs for every Mac configuration, from M1 to M4 Max. Learn how to optimize your setup for privacy and speed.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/scaling-mcp-with-tool-rag">How to Scale MCP to 100+ Tools</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Jan 5, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Avoid burning context and skyrocketing costs with massive Model Context Protocol (MCP) servers. Learn how to use Tool-RAG to scale to hundreds of tools efficie
16ntly.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-become-ai-engineer-roadmap">How to Become an AI Engineer in 2026 (Roadmap)</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Aaron T.<!-- --> <!-- -->on<!-- --> <!-- -->Jan 5, 2026</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Transition from an AI user to a builder with this technical roadmap designed for software engineers. Learn the essential skills, from Python mastery to RAG architecture.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/common-machine-learning-mistakes-beginners">12 Mistakes Learners Make When Getting Started with Machine Learning</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Aditya S.<!-- --> <!-- -->on<!-- --> <!-- -->Dec 9, 2025</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">The most common errors engineers make when transitioning to Machine Learning, from neglecting data cleaning to chasing state-of-the-art models, and learn actionable fixes to accelerate your progress.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-estimate-llm-time-to-first-token-ttft">How to Estimate LLM Time to First Token (TTFT)</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Nov 28, 2025</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Information on the mathematics behind estimating Time to First Token. We break down prefill dynamics, hardware scaling, and attention mechanisms to help you predict model latency without running the code.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/how-to-setup-oauth-chatgpt-connector">How to Set Up OAuth for the ChatGPT Connector (Updated: May 2026)</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Wei Ming T.<!-- --> <!-- -->on<!-- --> <!-- -->Oct 21, 2025</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">Step-by-step guide to walk you through the exact endpoints and response specs OpenAI expects, including the undocumented OIDC location and token exchange.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/why-not-one-using-mcp-yet">Why Is No One Using Model Context Protocol (Yet)</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Aaron T.<!-- --> <!-- -->on<!-- --> <!-- -->Oct 8, 2025</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">The Model Context Protocol (MCP) promises to unify AI tools, saving you money on subscriptions. So why has it failed to gain traction? We'll look at the technical and market hurdles holding it back.</p><div style="margin-top:calc(1rem * var(--mantine-scale));margin-bottom:calc(1rem * var(--mantine-scale))" class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div></div><div><a style="--text-fz:var(--mantine-font-size-xl);--text-lh:var(--mantine-line-height-xl);color:#7048e8;font-weight:700" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="xl" href="/posts/specific-terms-instead-of-ai">30 Precise Terms to Use Instead of 'AI'</a><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">By<!-- --> <!-- -->Jacob M.<!-- --> <!-- -->on<!-- --> <!-- -->Oct 6, 2025</p><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);padding-top:var(--mantine-spacing-sm)" class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-size="sm">How to stop saying 'AI' for everything. This guide gives you 30 specific machine learning terms that you can use to demonstrate proficiency.
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16ccount Settings\"},\"AboutPage\":{\"metaTitle\":\"About ApX Machine Learning\",\"metaDescription\":\"ApX Machine Learning is built for engineers who already know how to code and need to go deeper, from fine-tuning models to deploying AI in production systems.\",\"heroTitle\":\"About ApX Machine Learning\",\"heroSubtitle\":\"Hardware sizing tools, model specifications, and engineer-grade reference guides for LLM deployment.\",\"founderImageAlt\":\"Wei-Ming Thor\",\"founderName\":\"Wei-Ming Thor\",\"founderTitle\":\"Founder\",\"missionTitle\":\"The Mission\",\"missionText1\":\"ApX Machine Learning is built for engineers actively deploying models: developers who need direct answers on hardware fit, serving costs, and model selectionâalong with the mechanics underneath those numbers.\",\"missionText2\":\"That means tools that give you numbers you can act on. As a natural extension of these tools, we maintain a small library of deep, open-access engineering courses to help you understand the math and mechanics underneath the systems you deploy.\",\"whyExistsTitle\":\"Why This Exists\",\"whyExistsText1\":\"I originally built ApX Machine Learning as a set of AutoML (Automated Machine Learning) tools for my own personal use. As open-weights models like Llama and Qwen became viable to run locally, I started writing guides on how to run them and what GPUs they required.\",\"whyExistsText2\":\"The pivotal moment came with the launch of DeepSeek V3, when the industry realized open models could compete at the absolute top tier. At the time, ApX had the only comprehensive guide detailing the VRAM requirements and hardware constraints of running it, and traffic to the site surged.\",\"whyExistsText3\":\"Since then, ApX has transitioned from a small enthusiast resource into a professional hub helping developers deploy models at home and at a production scale. Today, it hosts the VRAM calculator, LLM directory, and reference guides used by thousands of engineers weekly.\",\"courseCreationTitle\":\"How the Data \u0026 Guides Are Built\",\"courseCreationText1\":\"Maintaining up-to-date hardware databases and engineering reference guides requires continuous research. To do this at scale without sacrificing technical accuracy, we use the same AI-assisted workflows we write about: specialized agents query academic papers and benchmark data, draft visualizations, and run verification checks.\",\"courseCreationText2\":\"The process consists of four main components:\",\"courseCreationComponent1Title\":\"Curated Knowledge Foundation\",\"courseCreationComponent1Text\":\"Content is grounded in academic papers, industry standards, and expert publications. A Retrieval-Augmented Generation (RAG) engine queries this knowledge base to ensure accuracy.\",\"courseCreationComponent2Title\":\"AI Agent Orchestration\",\"courseCreationComponent2Text\":\"Specialized agents handle different aspects: structure design, content writing, quality refinement, and data visualization. Each agent accesses the knowledge base to produce accurate, well-referenced material.\",\"courseCreationComponent3Title\":\"Quality Assurance Pipeline\",\"courseCreationComponent3Text\":\"Multi-layer accuracy checks validate content against sources. The enhancement stage optimizes for technical depth and precision.\",\"courseCreationComponent4Title\":\"Human Editorial Review\",\"courseCreationComponent4Text\":\"Every guide, tool update, and course goes through human editorial review before publication. Feedback is reviewed continuously to improve calculator accuracy and content depth.\",\"courseCreationText3\":\"This semi-automated pipeline allows a single engineer to maintain a massive, highly accurate resource that stays current as the local LLM landscape moves fast.\",\"kerbToolkitPromo\":\"The tools and utilities that drive these have been open sourced. See the\",\"learnMoreAboutKerb\":\"LLM Developer toolkit\",\"learnMoreAboutMethodology\":\"Learn More About the Methodology\",\"goalsTitle\":\"The Goals\",\"goalsText1\":\"Cover what most courses won't. The most important problems in applied AI aren't covered in introductory courses. ApX covers them: fine-tuning on constrained hardware, serving models efficie
16ntly, evaluating what benchmark scores actually mean for your use case.\",\"goalsText2\":\"Keep serious content free. The highest-quality technical ML education sits behind expensive subscriptions or buried in papers that assume institutional access. ApX keeps its core content free, so engineers anywhere, regardless of budget, can go as far as they need to.\",\"pillarsTitle\":\"What We Build\",\"toolsTitle\":\"Tools\",\"toolsText\":\"The VRAM Calculator answers the most practical questions in LLM deployment: will it run, and what will it cost.\",\"toolsCta\":\"Open the calculator â\",\"dataTitle\":\"Data\",\"dataText\":\"The LLM Directory, leaderboards, and trend data track every model that matters: specs, benchmarks, hardware requirements, and licensing.\",\"dataCta\":\"Explore models â\",\"educationTitle\":\"Education\",\"educationText\":\"A collection of free, in-depth guides and a masterclass for engineers who want to understand the math and details past the tutorials.\",\"educationCta\":\"Browse courses â\",\"independenceTitle\":\"Independence\",\"independenceText\":\"Rankings, benchmark results, and calculator output are never for sale. Ads are always labeled and never influence what the data says.\",\"openCalculator\":\"Open the VRAM Calculator\",\"startLearningTitle\":\"Start Here\",\"startLearningText\":\"The VRAM calculator and directory require no account. For extra depth on fine-tuning, inference optimization, or production deployment, feel free to check out the courses.\",\"aiEngineerRoadmap\":\"AI Engineer Roadmap\",\"browseCourses\":\"Browse Courses\"},\"landing\":{\"hero\":{\"headerStart\":\"Model LLM memory, throughput, and latency \",\"headerMiddle\":\"before provisioning compute.\",\"headerEnd\":\"\",\"description\":\"Simulate dynamic KV cache growth, interconnect bottlenecks, and concurrency latency cliffs with physical benchmark accuracy.\",\"primaryCta\":\"Open the LLM Calculator\",\"secondaryCta\":\"See the accuracy report\",\"selectSpec\":\"Serving Spec\",\"fits\":\"Performance Modeled\",\"model\":\"Model\",\"quantization\":\"Quantization\",\"preferredGpu\":\"Preferred GPU\",\"contextLength\":\"Context length\",\"concurrentUsers\":\"Concurrent users\",\"totalVram\":\"Total VRAM\",\"estThroughput\":\"Est. throughput\",\"estTtft\":\"Est. TTFT\",\"estHourlyCost\":\"Est. hourly cost\",\"openConfig\":\"Open this configuration â\",\"quantValue\":\"4-bit (INT4)\",\"throughputValue\":\"~{val} tok/s/user\",\"ttftValue\":\"~{val} ms\",\"hourlyCostValue\":\"$${val} / hr\",\"operatingPoint\":\"Operating Point\",\"operatingPointValue\":\"{users} users · ~{ttft}s TTFT\",\"safeKneeValue\":\"~{users} users\",\"insufficientVramValue\":\"\u003e{users} users\",\"modelSpec\":\"Llama-3-70B FP8 · 2x H100 · 32k context\"},\"setupMistake\":{\"title\":\"Open weights are free. GPU time isn't.\",\"caption\":\"Container pull â CUDA-matched wheels â Download 140 GB weights â Out of Memory\",\"col1Title\":\"The weights fit. The KV cache does not.\",\"col1Body\":\"At production context length and concurrency, KV cache memory scales linearly and exhausts VRAM. The out-of-memory crash arrives only after weights are loaded and traffic hits.\",\"col2Title\":\"The batch size 1 benchmark looked fine.\",\"col2Body\":\"Under concurrency, per-user tokens per second is a fraction of what a single-stream test showed. Queue times dominate as batch capacity saturates.\",\"col3Title\":\"Two GPUs were supposed to be twice as fast.\",\"col3Body\":\"Without NVLink, inter-GPU communication dominates. Tensor-parallel execution stalls waiting on PCIe bus bandwidth.\",\"footer\":\"None of this requires trial and error. You can model every constraint before provisioning.\"},\"readBeforeBill\":{\"title\":\"Get a projection before the bill.\",\"description\":\"Eliminate trial-and-error cluster sizing. Calculate what fits in memory, project hourly spend, evaluate concurrency limits, and verify whether adding a GPU increases throughput or only adds interconnect overhead.\",\"cta\":\"Calculate your model\",\"modelSpec\":\"Llama-3-70B FP8 · 32,768 context · 4 concurrent users\",\"memoryBreakdown\":\"Memory breakdown\",\"weightsLabel\":\"Weights: {val} GB\",\"kvCacheLabel\":\"Active KV: {val} GB\",\"poolLabel\":\"Available Pool: {val} GB\",\"activeVram\":\"Active VRAM\",\"activeVramSub\":\"{pct}% of {total} GB on {gpu}\",\"concurrencyLimit\":\"Concurrency Limit\",\"usersLabel\":\"concurrent users\",\"concurrencySub\":\"{headroom} GB dynamic headroom\",\"ttftLabel\":\"First-Token Latency (TTFT)\",\"ttftSub\":\"p50: {p50}s · p95: {p95}s\",\"throughput\":\"Est. Throughput\",\"throughputValue\":\"~{val} tok/s/user\",\"throughputSub\":\"{total} tok/s cluster throughput\"},\"threeResults\":{\"title\":\"Results worth checking before sizing a fleet.\",\"card1Title\":\"Aggregate throughput saturates. Per-user speed does not stop falling.\",\"card1Body\":\"Past the saturation batch, aggregate throughput flatlines while per-user latency spikes. The crossing point determines the optimal serving decision.\",\"card2Title\":\"Weight quantization does nothing for the KV cache.\",\"card2Body\":\"INT4 reduces base model weights, but KV cache remains uncompressed by default. At 32k or 128k context lengths, KV cache memory quickly overtakes weight memory.\",\"card3Title\":\"More GPUs do n
16ot mean more speed. Interconnect limits scaling.\",\"card3Body\":\"Scaling tensor parallelism across PCIe links introduces all-reduce latency that can offset additional compute.\",\"link1\":\"INT4 quantization quarters the batch size at which this throughput saturates, increasing serving capacity.\",\"link2\":\"KV cache quantization reduces memory footprint per user, unlocking larger concurrent batch sizes.\",\"link3\":\"Compare NVLink vs PCIe throughput scaling curves for tensor-parallel deployments.\",\"verifyAction\":\"Verify â\",\"totalThroughput\":\"Total tok/s\",\"tokensPerSec\":\"tokens / sec\",\"batchSize\":\"batch size\",\"saturation\":\"Saturation\",\"oneUser\":\"1 User\",\"eightUsers\":\"8 Users\",\"thirtyTwoUsers\":\"32 Users\",\"weights\":\"Weights (GB)\",\"kvCache\":\"KV Cache (GB)\",\"vramGb\":\"VRAM (GB)\",\"eightyGbLimit\":\"80GB Limit\",\"oneGpu\":\"1 GPU\",\"twoGpus\":\"2 GPUs\",\"fourGpus\":\"4 GPUs\",\"eightGpus\":\"8 GPUs\"},\"measuredRuns\":{\"title\":\"Validated against physical hardware benchmarks.\",\"description\":\"Predictions are verified against a regression suite of physical benchmark runs across hardware architectures, quantizations, and concurrency levels. We track and publish discrepancies transparently.\",\"sentenceHighlight\":\"Verify the numbers before provisioning an 8x or 16x GPU node.\",\"cta\":\"See the full accuracy report\",\"logosTitle\":\"Used by engineers and researchers at leading technology companies and universities worldwide\"},\"dataEstimates\":{\"title\":\"Architectural specifications behind the numbers.\",\"description\":\"Structured parameters for open-weight models: attention mechanisms, hidden dimensions, layer counts, context windows, and hardware requirements across quantization formats.\",\"linkExplorer\":\"Explore models directory â\",\"headerModel\":\"Model\",\"headerParams\":\"Params (total/active)\",\"headerAttention\":\"Attention\",\"headerContext\":\"Context\",\"headerVram\":\"VRAM at Q4\",\"headerRunsOn\":\"Runs on\"},\"courseReference\":{\"title\":\"Featured courses\",\"description\":\"A 30-chapter masterclass on building large language models, alongside courses on transformer architecture, attention mechanisms, weight quantization, and serving systems.\",\"highlight\":\"Explore the underlying model architecture in the courses.\",\"primaryCta\":\"Read: How to Build a Large Language Model\",\"secondaryCta\":\"Browse all courses â\",\"tracksLabel\":\"Course tracks\",\"roadmapLink\":\"Learning Roadmap\",\"viewCourse\":\"View Course\",\"readMasterclass\":\"Read Masterclass\",\"masterclassBadge\":\"Masterclass\",\"courses\":{\"transformerModels\":\"Introduction to Transformer Models\",\"transformerModelsDesc\":\"Grasp multi-head attention mechanisms, sequence length scaling, and KV cache memory footprint.\",\"practicalQuantization\":\"Practical Quantization for Large Language Models\",\"practicalQuantizationDesc\":\"Implement PTQ, QAT, FP8, and INT4 compression to reduce model size and accelerate compute.\",\"llmFineTuning\":\"Introduction to LLM Fine-Tuning\",\"quantizedDeployment\":\"Deploying Quantized LLMs for Efficient Inference\",\"quantizedDeploymentDesc\":\"Master tensor parallelism, continuous batching, and high-throughput inference serving.\",\"masterclass\":\"How to Build a Large Language Model\",\"masterclassDesc\":\"A 30-chapter masterclass constructing transformer layers, attention heads, and weights from scratch.\"},\"tracks\":{\"llm\":\"Large Language Models\",\"ml\":\"Machine Learning\",\"programming\":\"Programming\",\"math\":\"Mathematics\",\"data\":\"Data Engineering\"},\"topicsLabel\":\"Course topics\"},\"gating\":{\"title\":\"Need pipeline parallelism, multi-node topologies, or production traffic simulation?\",\"cta\":\"Explore Pro features\"},\"recentAnalysis\":{\"title\":\"Recent analysis\",\"viewAll\":\"View all posts\"},\"LandingSocialProof\":{\"title\":\"Trusted by ML Engineers and Researchers Worldwide\",\"subtitle\":\"Universities, research labs, and independent engineers across 100+ countries.\",\"showMore\":\"Show More\",\"showLess\":\"Show Less\",\"andMore\":\"...and more\"}
16,\"LandingCourseList\":{\"mostPopular\":\"Popular Courses\",\"noDescription\":\"No description available.\",\"approxHours\":\"Approx. {hours} hours\",\"viewCourse\":\"View Course\",\"viewAll\":\"View All Courses\"},\"LandingMasterclass\":{\"preTitle\":\"MASTERCLASS\",\"mainTitle1\":\"HOW TO BUILD A\",\"mainTitle2\":\"LARGE LANGUAGE MODEL\",\"chapterInfo\":\"30 Chapters, 700+ Pages of In-Depth Content\",\"subtitle\":\"Guide to understanding and building state-of-the-art language models\",\"prerequisites\":\"Prerequisites: Strong foundations in programming and deep learning\",\"readNow\":\"Read Now\"},\"LandingBlogPosts\":{\"recent\":\"Recent Articles \u0026 Insights\",\"viewAll\":\"View All Posts\"}},\"VramCalculator\":{\"meta\":{\"title\":\"LLM VRAM Calculator - Can I Run This LLM?\",\"description\":\"Calculate GPU VRAM requirements, dynamic KV cache, and tokens per second to see what LLMs can run on your Nvidia GPU or Apple Silicon.\"},\"quickAnswers\":{\"title\":\"Can I run this LLM?\",\"canIRun\":{\"question\":\"Can I run this LLM on my GPU?\",\"answer\":\"Select your model and GPU above to get an exact answer. The calculator accounts for weight precision, KV cache growth with context length, and framework overhead, not just a rough parameter-count estimate, so the fit/no-fit result reflects what will actually happen when you load the model.\"},\"howMuchVram\":{\"question\":\"How much VRAM do I need for a 7B, 13B, or 70B model?\",\"answer\":\"As a rough baseline at common quantization levels: a 7B model needs roughly 5-9GB, a 13B model roughly 9-16GB, and a 70B model roughly 40-70GB, depending on quantization (FP16 vs INT4/AWQ/GPTQ/GGUF) and how much context you plan to use. Use the calculator above for an exact figure for your specific model, quantization, and context length.\"},\"whatCanIRun\":{\"question\":\"What LLM can I run with my available VRAM?\",\"answer\":\"Enter your GPU (or Apple Silicon unified memory) above to see which open-weight models fit at each quantization level, along with expected tokens-per-second throughput, rather than checking model requirements one at a time.\"}},\"exportGated\":{\"title\":\"Unable to export\",\"pdfMessage\":\"Please update your payment details to enable PDF exports.\",\"csvMessage\":\"Please update your payment details to enable CSV exports.\"},\"title\":{\"inferenceCalculator\":\"LLM Inference: VRAM \u0026 Performance Calculator\",\"fullFineTuning\":\"Full Fine-tuning: VRAM Calculator\",\"loraFineTuning\":\"LoRA Fine-tuning: VRAM Calculator\",\"qloraFineTuning\":\"QLoRA Fine-tuning: VRAM Calculator\",\"resourceCalculator\":\"LLM Resource Calculator\",\"loading\":\"Loading...\",\"howCalculationsMade\":\"How Calculations Are Made\",\"recentUpdates\":\"Version History\",\"versionHistory\":\"Version History\",\"versionHistoryDescription\":\"v4.0 (Sep 26, 2026)\",\"apiDocumentation\":\"API Docs\",\"apiDocumentationDesc\":\"Programmatic VRAM calculation API\"},\"table\":{\"version\":\"Version\",\"date\":\"Date\",\"description\":\"Changes\",\"suite\":\"Evaluation Suite\",\"cases\":\"Scored Cases\",\"worstCase\":\"Worst Case\"},\"description\":{\"calculationExplanation\":\"Memory usage is estimated using models that factor in architecture (parameters, layers, hidden dimensions, active experts, etc.), quantization, sequence length, and batch size. Performance estimates consider model/hardware analysis and benchmarks, though benchmark accuracy varies. Results are approximate.\",\"learnMore\":\"Learn more about how VRAM requirements are calculated â\",\"followLinkedIn\":\"Follow For Updates\",\"lookingForWork\":\"Looking for Remote Work (GMT+8)\",\"offloadingNote\":\"* Memory breakdown includes offloaded portions.\"},\"updates\":{\"october1\":\"Add Mamba architecture and SGLang runtime support.\",\"september26\":\"Decouple serving runtime, optimization traits, and core calculation engines into modular layers. Add framework preset engine with calibrated defaults for vLLM, TensorRT-LLM, llama.cpp, SGLang, HuggingFace, and Unsloth. Support fine-grained runtime trait overrides and expose framework presets in the calculation API.\",\"september21\":\"Add graph capture vs eager dispatch controls and chunked prefill prompt budgeting. Calibrate runtime performance and memory dynamics across unfused attention, multi-batch allocator retention, sub-2B Tensor Core scaling, and standard QLoRA weight layouts.\",\"september14\":\"Remove legacy static modality multipliers on model weights; no longer needed with improved architectural modeling.\",\"september11\":\"Add Compressed Sparse Attention 2 (CSA2) support with cross-layer KV cache reuse for DeepSeek V4.1\",\"september9\"
16:\"Bound multi-GPU Tensor Parallelism and Pipeline Parallelism communication buffers by chunked prefill sequence length, preventing inflated overhead calculations on long-context distributed configurations.\",\"september7\":\"Add SLA controls and safe operating knee estimation to concurrency scaling analysis. Calibrate decode TPS curves and KV block pool capacity limits across serving configurations.\",\"september5\":\"Fix KV cache sizing for Multi-Head Latent Attention (MLA) and hybrid linear attention models when explicit head dimensions are configured.\",\"august18\":\"Add support for block-parallel speculative decoding (DFlash, Medusa, Eagle) with exact geometric token expectation models. Calibrate per-layer KV cache arithmetic for explicit head dimension configurations.\",\"august7\":\"Calibrate fine-tuning training speed calculations and fix multi-node ZeRO-3 communication scaling.\",\"august4\":\"Multi-GPU accuracy and speed calibration:\\n⢠Scale activations and logits buffer memory by DP replica count under replicated inference parallelism\\n⢠Correct host offloading speed models by decoupling PCIe transfer bottlenecks from GPU counts\\n⢠Calculate cluster speed and overhead boundaries using active GPU counts rather than total physical capacity\\n⢠Partition QLoRA buffer overhead using local sharded parameters rather than global counts\",\"august3\":\"Add setup planner to compare hardware configurations, optimize cluster scaling, and estimate throughput and cost trade-offs.\\n\\nBug fixes:\\n⢠Fix multi-GPU throughput and training speed estimation bugs, including KV cache offload bottlenecks, per-device batch size, and gradient accumulation scaling\\n⢠Fix KV cache calculations for DeepSeek Sparse Attention (DSA) architectures\",\"july24\":\"Fix static shared memory and per-user KV cache memory allocations in per-GPU view. Correct memory bandwidth specifications for Ada and Blackwell chips.\",\"july20\":\"Calibrate TTFT (Time to First Token) and prefill speed, fix batch-size scaling behavior under serving concurrency, and add quadratic attention term\",\"july15\":\"Improve TPS calculation performance using memory-bandwidth roofline model and fix activation sharding across tensor parallel and pipeline parallel domains\",\"july9\":\"Add system reserve deduction, add fine-tuning offloading\",\"july7\":\"Add support for hybrid attention layers, sliding window and linear attention\",\"july5\":\"Add auto-fix optimization for cluster topologies, support Apple Silicon Thunderbolt 4/5 interconnects, and auto-disable cluster mode for Mac devices.\",\"july4\":\"Calibrate Tensor Parallelism scaling and fix communication penalty constant for slow interconnects\",\"june29\":\"Add multi node cluster support\",\"june19\":\"Add Inference Parallelism options for Multi GPU inference\",\"june15\":\"Updates:\\n⢠Add hardware configurations saving to user profiles\\n⢠Add support for NVLink Gen 5 and Gen 6 interconnect types\\n⢠Add continuous batching simulation support\\n⢠Calibrate KV cache memory scaling for various attention architectures\\n⢠Improve batch calculation accuracy and limit thresholds\",\"june12\":\"Improve TPS calculation to account for memory-bandwidth limits at high batch sizes, including MoE models.\",\"april3\":\"Fix memory calculation with fine-tuning with gradient accumulation.\",\"february18\":\"Improve batch size scaling for fine-tuning.\",\"february3\":\"Add training cost estimation calculation.\",\"december8\":\"Fix per-user speed calculation to properly account for queuing when concurrent users exceed batch size.\",\"june7\":\"Fix KV cache calculation for non-MHA attention structures.\",\"june4\":\"Improve calculation speed.\",\"may27\":\"Add memory offloading options for CPU RAM and NVMe storage.\",\"may10\":\"Fix DeepSeek V3 MOE calculation bug. Improve MOE calculation and enable precision selection for fine-tuning.\",\"may6\":\"Add KV cache quantization customization.\"},\"updatesDate\":{\"october1\":\"Oct 1, 2026\",\"september26\":\"Sep 26, 2026\",\"september21\":\"Sep 21, 2026\",\"september14\":\"Sep 14, 2026\",\"september11\":\"Sep 11, 2026\",\"september9\"
16:\"Sep 9, 2026\",\"september7\":\"Sep 7, 2026\",\"september5\":\"Sep 5, 2026\",\"august18\":\"Aug 18, 2026\",\"august7\":\"Aug 7, 2026\",\"august4\":\"Aug 4, 2026\",\"august3\":\"Aug 3, 2026\",\"july24\":\"Jul 24, 2026\",\"july20\":\"Jul 20, 2026\",\"july15\":\"Jul 15, 2026\",\"july9\":\"Jul 9, 2026\",\"july7\":\"Jul 7, 2026\",\"july5\":\"Jul 5, 2026\",\"july4\":\"Jul 4, 2026\",\"june29\":\"Jun 29, 2026\",\"june19\":\"Jun 19, 2026\",\"june15\":\"Jun 15, 2026\",\"june12\":\"Jun 12, 2026\",\"april3\":\"Apr 3, 2026\",\"february18\":\"Feb 18, 2026\",\"february3\":\"Feb 3, 2026\",\"december8\":\"Dec 8, 2025\",\"june7\":\"Jun 7, 2025\",\"june4\":\"Jun 4, 2025\",\"may27\":\"May 27, 2025\",\"may10\":\"May 10, 2025\",\"may6\":\"May 6, 2025\"},\"quantizationDescriptions\":{\"inference\":\"Precision for model weights during inference. Lower uses less VRAM but may affect quality.\",\"qloraFineTuning\":\"QLoRA uses 4-bit base model weights (fixed).\",\"loraFineTuning\":\"LoRA uses FP16/BF16 base model weights (fixed).\",\"fullFineTuning\":\"Precision for base model weights during full fine-tuning. Affects memory for weights, optimizer, and gradients.\"},\"attentionStructures\":{\"mha\":\"Multi-Head Attention: Standard attention mechanism where each head attends to all positions independently.\",\"mqa\":\"Multi-Query Attention: Uses multiple query heads but a single key and value head, reducing KV cache size.\",\"mla\":\"Multi-head Latent Attention: Attention mechanism that often uses a compressed latent space for keys and values to improve efficiency and reduce KV cache, especially for long contexts.\",\"gqa\":\"Grouped-Query Attention: Query heads are grouped, and each group shares a single key and value head.\",\"dsa\":\"DeepSeek Sparse Attention: Fine-grained dynamic sparse attention that selectively retrieves relevant KV tokens to reduce memory and compute.\",\"csa2\":\"Compressed Sparse Attention 2: Dynamic sparse attention featuring cross-layer KV cache reuse to minimize memory footprint.\",\"swa\":\"Single-Head Attention: Simple attention mechanism with only one attention head.\",\"default\":\"Attention mechanism used by the model.\"},\"memoryStatus\":{\"insufficient\":\"Insufficient\",\"very_high\":\"Very High\",\"high\":\"High\",\"moderate\":\"Moderate\",\"ready\":\"Ready\",\"error\":\"Error\"},\"apiLimit\":{\"title\":\"Use More, Create an Account\",\"message\":\"You've reached the usage limit for unregistered visitors. Please create an account for unlimited access.\",\"login\":\"Login\",\"signup\":\"Sign Up (Free)\"},\"moeExpertTooltip\":\"While only a few experts are active per token during inference, all experts are still loaded into memory to enable fast switching.\",\"feedback\":{\"submittedTitle\":\"Submitted Feedback\",\"submitSuccess\":\"Thank you for your feedback! Appreciate your input.\",\"submitError\":\"Failed to submit feedback. Please try again.\"},\"share\":{\"button\":\"Share\",\"buttonLong\":\"Share\",\"copiedTitle\":\"Link Copied!\",\"copiedMessage\":\"Link copied to clipboard\",\"errorTitle\":\"Copy Failed\",\"errorMessage\":\"Failed to copy link. Please try manually.\"},\"configs\":{\"menuButton\":\"Configs\",\"saveCurrent\":\"Save as new config...\",\"updateCurrent\":\"Save changes to \\\"{name}\\\"\",\"saveButton\":\"Save\",\"savedLabel\":\"Saved Configurations\",\"saveModalTitle\":\"Save Configuration\",\"updateModalTitle\":\"Update Saved Configuration\",\"nameLabel\":\"Configuration Name\",\"namePlaceholder\":\"e.g. Llama 3.1 70B vLLM Serving\",\"signinToSaveConfigTooltip\":\"Sign in to save this configuration to your account.\",\"configLimitTooltip\":\"ApX Pro required to save more than 1 config.\",\"saveSuccess\":\"Configuration saved!\",\"loadedSuccess\":\"Loaded configuration: {name}\",\"saveError\":\"Failed to save configuration.\",\"deleteConfirmTitle\":\"Delete Saved Configuration\",\"deleteConfirmMessage\":\"Are you sure you want to delete this saved configuration?\",\"cancel\":\"Cancel\",\"save\":\"Save\",\"delete\":\"Delete\"},\"inputs\":{\"sharedConfigBanner\":{\"title\":\"Pro Configuration\",\"description\":\"This production-scale configuration is free to view. Upgrade to edit, or clear Pro options to customize it under the basic tier.\",\"upgrade\":\"Upgrade\",\"reset\":\"Clear Pro Options\"},\"setupType\":\"Setup Type\",\"singleRig\":\"Single Rig\",\"cluster\":\"Cluster\",\"numNodes\":\"Number of Nodes\",\"numNodesDescription\":\"Number of physical server chassis in the cluster\",\"gpusPerNode\":\"GPUs per Node\",\"gpusPerNodeDescription\":\"Number of GPUs installed in each node\",\"tpDegree\"
16:\"Tensor Parallelism (TP) Degree\",\"tpDegreeDescription\":\"Number of GPUs that share tensor computations for a single layer\",\"ppDegree\":\"Pipeline Parallelism (PP) Degree\",\"ppDegreeDescription\":\"Number of pipeline stages to split model layers sequentially\",\"interNodeInterconnect\":\"Inter-Node Network Interconnect\",\"interNodeInterconnectDescription\":\"Network connection between nodes in the cluster\",\"clusterModeDisabledForApple\":\"Apple Silicon is not supported in Cluster Mode\",\"calcMode\":{\"inference\":\"Inference\",\"finetuning\":\"Fine-tuning\"},\"kvCacheQuantizationFp16\":\"FP16 / BF16\",\"kvCacheQuantizationDefault\":\"Default\",\"kvCacheQuantizationExperimental\":\"Experimental\",\"quantizationGroups\":{\"standard\":\"Standard\",\"gguf\":\"GGUF\",\"legacyGguf\":\"Legacy GGUF\"},\"finetuningMethod\":\"Fine-tuning Method\",\"trainingMode\":\"Training Mode\",\"trainingModeDescription\":\"Choose how the model will be 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Model\",\"renameCustomModel\":\"Rename\",\"renameCustomModelTitle\":\"Rename Custom Model\",\"customModelNameLabel\":\"Model Name\",\"customModelNamePlaceholder\":\"e.g., My Fine-tuned Llama 3\",\"customModelUnsaved\":\"Custom Model (Unsaved)\",\"customModelsGroup\":\"Custom Models\",\"presetModelsGroup\":\"Preset Models\",\"deleteCustomModel\":\"Delete\",\"deleteCustomModelTitle\":\"Delete Custom Model\",\"deleteCustomModelConfirm\":\"Are you sure you want to delete \\\"{name}\\\"?\",\"deleteCustomModelCancel\":\"Cancel\",\"signinToSaveCustomModelTooltip\":\"Sign in to save custom models\",\"customModelModalityText\":\"Text\",\"customModelModalityOptions\":{\"text\":\"Text\",\"multimodal\":\"Multimodal\",\"vision\":\"Vision\",\"audio\":\"Audio\",\"video\":\"Video\"},\"customModelAuxiliaryParamsLabel\":\"Auxiliary Parameters\",\"customModelAuxiliaryParamsDescription\":\"Vision/audio encoders and projector weights in billions\",\"customModelDefaultVal\":\"(Default: {val})\",\"customModelDefaultOff\":\"(Default: Off)\",\"customModelParamsLabel\":\"Total Parameters\",\"customModelParamsDescription\":\"Total model parameter count in billions\",\"customModelArchitectureLabel\":\"Architecture\",\"customModelArchitectureDescription\":\"Dense or Mixture of Experts structure\",\"customModelDense\":\"Dense\",\"customModelMoe\":\"Mixture of Experts\",\"customModelExpertParamsLabel\":\"Active Expert Parameters\",\"customModelExpertParamsDescription\":\"Parameters activated per token in billions\",\"customModelAttentionStructureLabel\":\"Attention Structure\",\"customModelAttentionStructureDescription\":\"Attention mechanism architecture\",\"customModelAttentionOptions\":{\"gqa\":\"Grouped Query Attention (GQA)\",\"mha\":\"Multi-Head Attention (MHA)\",\"mqa\":\"Multi-Query Attention (MQA)\",\"mla\":\"Multi-Head Latent Attention (MLA)\",\"dsa\":\"DeepSeek Sparse Attention (DSA)\",\"csa2\":\"Compressed Sparse Attention 2 (CSA2)\",\"swa\":\"Sliding Window Attention (SWA)\"},\"customModelAdvancedToggle\":\"Advanced Architecture Overrides\",\"customModelLayersLabel\":\"Transformer Layers\",\"customModelLayersDescription\":\"Number of sequential transformer layers\",\"customModelHiddenDimLabel\":\"Hidden Dimension\",\"customModelHiddenDimDescription\":\"Model hidden dimension size\",\"customModelFfnIntermediateLabel\":\"FFN Intermediate Size\",\"customModelFfnIntermediateDescription\":\"Feed-forward network hidden dimension\",\"customModelVocabSizeLabel\":\"Vocabulary Size\",\"customModelVocabSizeDescription\":\"Tokenizer vocabulary size\",\"customModelNumAttentionHeadsLabel\":\"Query Attention Heads\",\"customModelNumAttentionHeadsDescription\":\"Total query attention heads\",\"customModelNumKeyValueHeadsLabel\":\"Key Value Heads\",\"customModelNumKeyValueHeadsDescription\":\"Total key and value attention heads\",\"customModelNumKeyValueHeadsMqaTooltip\":\"Multi-Query Attention (MQA) strictly uses 1 Key-Value head shared across all Query heads.\",\"customModelNumKeyValueHeadsMhaTooltip\":\"Multi-Head Attention (MHA) sets the number of Key-Value heads equal to the Query attention heads.\",\"customModelSlidingWindowSizeLabel\":\"Sliding Window Size\",\"customModelSlidingWindowSizeDescription\":\"Attention context window token limit\",\"customModelLinearAttentionRatioLabel\":\"Linear Attention Ratio\",\"customModelLinearAttentionRatioDescription\":\"Percentage of hybrid linear attention layers\",\"customModelAttentionParametersTitle\":\"Attention Parameters\",\"customModelKvSourceLayerCountLabel\":\"KV Source Layers\",\"customModelKvSourceLayerCountDescription\":\"Number of layers generating shared KV cache states\",\"customModelKvLatentDimLabel\":\"KV Latent Dimension\",\"customModelKvLatentDimDescription\":\"Latent compressed KV dimension\",\"customModelIndexerDimLabel\":\"Indexer Key Dimension\",\"customModelIndexerDimDescription\":\"Sparse routing indexer key dimension\",\"customModelPositionEmbeddingLabel\":\"Position Embedding\",\"customModelPositionEmbeddingDescription\":\"Positional encoding method\",\"customModelPositionEmbeddingOptions\":{\"rope\":\"Rotary Position Embedding (RoPE)\",\"alibi\":\"Attention with Linear Biases (ALiBi)\",\"absolute\":\"Absolute Position Embedding\",\"relative\":\"Relative Position Embedding\",\"xpos\":\"Exponential Position Embedding (xPos)\"},\"customModelModalityLabel\":\"Modality\",\"customModelModalityDescription\":\"Primary input and output modality\",\"customModelAutoDefault\":\"Default\",\"inferenceQuantization\":\"Inference Quantization\",\"inferenceQuantizationPlaceholder\":\"Select quantization\",\"baseModelPrecision\":\"Base Model Precision\",\"baseModelPrecisionPlaceholder\":\"Base model precision\",\"kvCacheQuantization\":\"KV Cache Quantization\",\"kvCacheQuantizationPlaceholder\":\"Select KV cache precision\",\"kvCacheQuantizationDescription\":\"KV Cache precision. Lower values reduce VRAM, especially for long sequences.\",\"runtimeSettings\":\"Advanced Runtime Settings\",\"resetRuntimeSettings\":\"Reset to Default\",\"executionMode\":\"Execution Mode\",\"executionModeDescription\":\"Graph captured or dynamic eager kernel dispatch\",\"executionModePlaceholder\":\"Select execution mode\",\"executionModeTooltip\":\"Graph Captured models pre-allocated execution graphs with minimal dispatch overhead, standard for most production optimized inference engines. Dynamic Eager models sequential dispatch overhead typical of eager PyTorch and Hugging Face generation loops.\",\"executionModeOptions\":{\"captured\":\"Graph Captured\",\"eager\":\"Dynamic Eager\"}
16,\"prefillMode\":\"Prefill Mode\",\"prefillModeDescription\":\"Chunked prompt processing or full sequence activation retention\",\"prefillModePlaceholder\":\"Select prefill mode\",\"prefillModeTooltip\":\"Chunked splits prompt processing into token budgets so peak activation memory stays bounded on long inputs, standard for most production optimized inference engines. Full Sequence processes the entire prompt at once, causing activation memory to scale with sequence length, typical of eager PyTorch and Hugging Face generation loops.\",\"prefillModeOptions\":{\"chunked\":\"Chunked\",\"full\":\"Full Sequence\"},\"prefillChunkSize\":\"Prefill Chunk Size\",\"prefillChunkSizePlaceholder\":\"Select chunk size\",\"prefillChunkSizeDescription\":\"Token budget per chunk during prompt processing\",\"prefillChunkSizeTooltip\":\"Number of prompt tokens evaluated per iteration. 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Affects whether the model, KV cache, and activations fit in memory.\",\"customGpuSubtext\":\"Configure custom memory and compute performance.\",\"customBandwidth\":\"Custom Bandwidth (GB/s)\",\"customBandwidthDescription\":\"Memory bandwidth. Affects token generation speed and inter-token latency during decoding.\",\"customTflops\":\"Custom TFLOPS\",\"customTflopsDescription\":\"Peak dense FP16/BF16 Tensor Core performance. Using FP32 or sparse rates will result in inaccurate speed predictions.\",\"customTflopsTooltip\":\"Peak dense FP16/BF16 Tensor Core performance, excluding standard FP32 or sparse rates.\",\"activeGpuSpecs\":\"GPU Specifications\",\"unifiedMemory\":\"Unified Memory (GB)\",\"unifiedMemoryDescription\":\"Unified memory capacity for Apple Silicon, shared by CPU and GPU. Affects maximum loadable model and KV cache size.\",\"unifiedMemoryBandwidth\":\"Unified Memory Bandwidth (GB/s)\",\"unifiedMemoryBandwidthDescription\":\"Unified memory bandwidth for Apple Silicon. Affects token generation speed and inter-token latency during decoding.\",\"unifiedMemoryTflops\":\"GPU Compute (TFLOPS)\",\"unifiedMemoryTflopsDescription\":\"Apple Silicon GPU peak dense FP16 compute performance. Using FP32 rates will result in inaccurate speed predictions.\",\"unifiedMemoryTflopsTooltip\":\"Apple Silicon GPU peak dense FP16 compute performance, excluding standard FP32 rates.\",\"numDevices\":\"Num Devices\",\"numGpus\":\"Num GPUs\",\"numDevicesDescription\":\"Devices for parallel training\",\"numGpusDescription\":\"Devices for parallel inference\",\"batchSize\":\"Batch Size\",\"batchSizePlaceholder\":\"Enter batch size\",\"batchSizeDescriptionInference\":\"Inputs processed simultaneously per step\",\"batchSizeDescriptionFinetuning\":\"Samples per optimization step\",\"batchSizeDescriptionFinetuningWithAccumulation\":\"{description} (Effective Batch: {effectiveBatch})\",\"sequenceLength\":\"Sequence Length\",\"sequenceLengthPlaceholder\":\"Enter sequence length\",\"sequenceLengthDescriptionInference\":\"Context window size. Includes input and output tokens.\",\"sequenceLengthDescriptionFinetuning\":\"Max tokens per training sample\",\"showScalingChart\":\"Scaling curve\",\"hideScalingChart\":\"Hide curve\",\"scalingChartTitle\":\"Memory Scaling by Sequence Length\",\"scalingChartSubtitle\":\"Estimated VRAM breakdown across sequence lengths based on current model architecture and quantization.\",\"chartLimitLabel\":\"Limit: {max}\",\"batchScalingChartTitle\":\"Memory Scaling by Batch Size\",\"batchChartModeOverhead\":\"Overhead\",\"batchChartOverheadBudget\":\"Overhead Budget\",\"concurrentUsersScalingChartTitle\":\"Memory Scaling by Concurrent Users\",\"scalingChartWeights\":\"Model Weights\",\"scalingChartKvCache\":\"KV Cache\",\"scalingChartOverhead\":\"Overhead \u0026 Activations\",\"gpuVramLimit\":\"Available VRAM\",\"gpuMemoryLimit\":\"Available Memory\",\"reportDiscrepancy\":\"Report discrepancy\",\"currentContext\":\"Current Context\",\"maxUsableContext\":\"Max usable context: ~{tokens} tokens\",\"maxContextBadge\":\"Max Context: {max}\",\"chartModeTotal\":\"Total VRAM\",\"chartModeTotalMemory\":\"Total Memory\",\"chartModeKv\":\"KV Cache\",\"chartModeActivations\":\"Activations\",\"viewModeVram\":\"VRAM Scaling\",\"viewModeMemory\":\"Memory Scaling\",\"viewModeLatency\":\"Latency (TTFT)\",\"viewModeKvBlocks\":\"KV Block Pool\",\"kvBlocksTooltip\":\"Inference engines divide KV cache memory into fixed-size pages called blocks, typically 16 tokens each. When all blocks are allocated, requests wait in queue or risk preemption.\",\"latencyCliffTitle\":\"TTFT Under Concurrency\",\"isolateKvCache\":\"KV cache only\",\"riskNone\":\"Low\",\"riskModerate\":\"Moderate\",\"riskCritical\":\"High\",\"ttftStatusStable\":\"Stable\",\"ttftStatusSaturated\":\"KV Saturated\",\"ttftStatusOom\":\"Exceeds VRAM\",\"ttftStatusOomMemory\":\"Exceeds Memory\",\"ttftStatusOomDesc\":\"Selected concurrency exceeds available GPU VRAM. Curve reflects theoretical compute latency.\",\"ttftStatusOomDescMemory\":\"Selected concurrency exceeds available memory. 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Additional requests will experience queue latency or preemption.\",\"viewScalingChartLink\":\"View\",\"insufficientVramNotice\":\"Insufficient VRAM\",\"insufficientMemoryNotice\":\"Insufficient Memory\",\"p50Ttft\":\"P50 TTFT\",\"p95Ttft\":\"P95 TTFT\",\"safeOperatingKnee\":\"Safe Operating Knee\",\"preemptionRisk\":\"Preemption Risk\",\"totalKvBlocks\":\"Total KV Blocks\",\"usedKvBlocks\":\"Allocated Blocks\",\"activeKvBlocks\":\"Active at {users} users\",\"allocationPerUser\":\"Allocation per User\",\"blocksUnit\":\"blocks\",\"tokensPerStream\":\"tokens/stream\",\"usersUnit\":\"users\",\"qpsAssumptionNote\":\"at 150 tok/req\",\"freeKvBlocks\":\"Free Blocks\",\"maxSustainableQps\":\"Est. Sustainable QPS\",\"kvBudgetLimit\":\"KV Budget\",\"activationBudgetLimit\":\"Activation Budget\",\"slidingWindowBadge\":\"SWA ({size})\",\"slidingWindowTooltip\":\"Sliding Window Attention limits active KV cache retention to {size} tokens.\",\"enableLongSequence\":\"Long sequence\",\"concurrentUsers\":\"Concurrent Users\",\"concurrentUsersPlaceholder\":\"Enter number of concurrent users\",\"concurrentUsersDescription\":\"Number of users running inference simultaneously\",\"gradientAccumulation\":\"Gradient Accumulation\",\"gradientAccumulationTooltip\":\"Process batches in smaller steps before updating model weights. Reduces memory at the cost of speed.\",\"gradientAccumulationDescription\":\"Steps to accumulate gradients before optimizer update.\",\"enableOffloading\":\"Enable Offloading to CPU/RAM or NVMe\",\"offloadTarget\":\"Offload Target\",\"estimatedRam\":\"Estimated {target} required: ~{amount} GB\",\"offloadedComponents\":\"Offloaded Components\",\"enabled\":\"Enabled\",\"disabled\":\"Disabled\",\"offloadedWeights\":\"Model Weights\",\"offloadedOptimizerStates\":\"Optimizer States\",\"offloadedGradients\":\"Gradients\",\"tooltipOffloadOptimizerDisabled\":\"Optimizer state offloading requires ZeRO-1/2/3 to partition optimizer states across nodes.\",\"tooltipOffloadGradients
16DisabledStage\":\"Gradient offloading requires ZeRO-2/3 to partition gradients across nodes.\",\"tooltipOffloadWeightsDisabledStage\":\"Model weight offloading requires ZeRO-3 to partition parameters across nodes.\",\"tooltipOffloadRequiresOptimizer\":\"Requires optimizer state offloading.\",\"tooltipOffloadOptimizer\":\"Offload optimizer states to host CPU RAM or NVMe.\",\"tooltipOffloadGradients\":\"Offload gradients to host CPU RAM or NVMe.\",\"tooltipOffloadWeights\":\"Offload model parameters/weights to host CPU RAM or NVMe.\",\"activationOffloadingProTooltip\":\"Offload activations to fine-tune larger models or context lengths without out-of-memory errors. Requires ApX Pro.\",\"offloadingTooltip\":\"Offload parameters, cache, or training states to system memory or storage.\",\"offloadModelWeights\":\"Offload Model Weights\",\"offloadByLayers\":\"By Number of Layers\",\"offloadByPercentage\":\"By Percentage\",\"numLayersToOffload\":\"Number of Layers to Offload:\",\"offloadedLayers\":\"Offloaded Layers: {offloadLayers} / {totalLayers} ({percent}%)\",\"percentageOfModelToOffload\":\"Percentage of Model to Offload:\",\"offloadingPercentage\":\"Offloading {percent}% of model\",\"offloadKvCache\":\"Offload KV Cache to CPU/RAM\",\"offloadKvCacheTooltip\":\"Offloads the Key-Value cache to CPU RAM. This can save significant VRAM for long contexts but will slow down token generation speed.\",\"enablePrefixCaching\":\"Prefix Cache Sharing\",\"sharedPrefixRatio\":\"Shared Prefix Percentage\",\"sharedPrefixRatioTooltip\":\"Shares cached prompt prefix across users to reduce KV Cache memory.\",\"enableContinuousBatching\":\"Continuous Batching\",\"enableContinuousBatchingTooltip\":\"Continuous batching schedules execution per iteration instead of per request, reducing activation memory overhead for concurrent requests.\",\"enableStaticVramPreallocation\":\"Static VRAM Pre-allocation\",\"staticVramPreallocationTooltip\":\"Use this pre-allocation strategy to avoid dynamic memory allocation overhead and runtime Out-Of-Memory (OOM) errors.\",\"gpuMemoryUtilization\":\"GPU Memory Utilization\",\"enableSpeculativeDecoding\":\"Speculative Decoding\",\"speculativeDecodingTooltip\":\"Speculative decoding accelerates LLM inference by using a fast, smaller draft model to propose tokens, which are verified in parallel by the larger target model. Improves generation speed on network-bottlenecked configurations.\",\"speculativeDraftModelParams\":\"Draft Model Size\",\"speculativeDraftModelParamsDesc\":\"Size of the smaller draft model used to propose candidate tokens\",\"speculativeAcceptanceRate\":\"Draft Token Acceptance Rate\",\"speculativeAcceptanceRateDesc\":\"Average probability that a proposed token from the draft model is accepted by the target model\",\"speculativeDraftTokens\":\"Draft Tokens per Step\",\"speculativeDraftTokensDesc\":\"Number of tokens the draft model proposes in each speculation step\",\"speculativeType\":\"Drafting Architecture\",\"speculativeTypeDesc\":\"Choose between standard sequential drafting or block-parallel drafting.\",\"speculativeTypeSequential\":\"Sequential (Standard)\",\"speculativeTypeDflash\":\"Block-Parallel (DFlash / Medusa)\",\"interconnectType\":\"GPU Interconnect Type\",\"interconnectTypeDescription\":\"Connection type between GPUs. Higher bandwidth enables better scaling efficiency\",\"scalingEfficiency\":\"Scaling factor\",\"interconnectGroups\":{\"highSpeed\":\"High-Speed Interconnects\",\"pcie\":\"PCIe\",\"network\":\"Network-Based\"},\"interconnectTypes\":{\"nvlinkGen6\":\"NVLink Gen 6 (3.6 TB/s)\",\"nvlinkGen5\":\"NVLink Gen 5 (1.8 TB/s)\",\"nvlinkGen4\":\"NVLink Gen 4 (900 GB/s)\",\"nvlinkGen3\":\"NVLink Gen 3 (600 GB/s)\",\"nvlinkGen2\":\"NVLink Gen 2 (300 GB/s)\",\"nvlinkBridge\":\"NVLink Bridge (100 GB/s)\",\"infinibandHdr\":\"InfiniBand HDR (200 Gb/s)\",\"infinibandEdr\":\"InfiniBand EDR (100 Gb/s)\",\"pcie5\":\"PCIe 5.0 x16 (128 GB/s)\",\"pcie4\":\"PCIe 4.0 x16 (64 GB/s)\",\"pcie3\":\"PCIe 3.0 x16 (32 GB/s)\",\"thunderbolt5\":\"Thunderbolt 5 (80-120 Gb/s)\",\"thunderbolt4\":\"Thunderbolt 4/3 (40 Gb/s)\",\"ethernet400g\":\"400 Gigabit Ethernet\",\"ethernet200g\":\"200 Gigabit Ethernet\",\"ethernet100g\":\"100 G
16igabit Ethernet\",\"ethernet25g\":\"25 Gigabit Ethernet\",\"ethernet10g\":\"10 Gigabit Ethernet\",\"ethernet1g\":\"1 Gigabit Ethernet\"},\"kerbPromo\":{\"badge\":\"Open Source\",\"title\":\"Kerb: LLM Development Toolkit\",\"description\":\"Python toolkit for building production-ready LLM applications. Modular utilities for RAG, Agents, and Structured Outputs.\"},\"submitFeedback\":\"Submit Feedback / Report Issue\",\"saveRigToProfile\":\"Save setup\",\"savedRig\":\"Saved\",\"profiles\":{\"menuButton\":\"Profiles\",\"menuButtonDefault\":\"Saved Profiles\",\"menuLabel\":\"Saved Hardware Profiles\",\"clearSelection\":\"Clear Selection\",\"saveModalTitle\":\"Save Hardware Profile\",\"updateModalTitle\":\"Update Hardware Profile\",\"nameLabel\":\"Profile Name\",\"namePlaceholder\":\"e.g. Home GPU Server, Cloud H100 Server\",\"cancel\":\"Cancel\",\"save\":\"Save\",\"deleteConfirmTitle\":\"Delete Hardware Profile\",\"deleteConfirmMessage\":\"Are you sure you want to delete this hardware profile?\",\"delete\":\"Delete\"},\"saveRigTooltip\":\"Save this hardware setup as your default profile configuration.\",\"saveRigClusterUnsupportedTooltip\":\"Saving setups in cluster mode is not yet supported.\",\"signinToSaveRigTooltip\":\"Sign in to save this hardware setup as your default profile configuration.\",\"saveRigSuccess\":\"Setup saved to profile!\",\"signinToSaveRig\":\"Sign in to save setup\",\"savingRig\":\"Saving setup...\",\"logScaleProTooltip\":\"Log scale requires ApX Pro.\",\"logScaleProMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to use log scale.\",\"continuousBatchingProTooltipMsg\":\"Continuous batching requires ApX Pro for multi-user scale simulation.\",\"batchSizeProTooltip\":\"Batch sizes above {max} require ApX Pro.\",\"batchSizeProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate batch sizes \u003e {max} for production use.\",\"concurrentUsersProTooltip\":\"Simulating more than {max} concurrent users requires ApX Pro.\",\"concurrentUsersProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate \u003e {max} concurrent users for production use.\",\"latencyProMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to set custom latency targets.\",\"latencyProTooltip\":\"Custom latency targets require ApX Pro.\",\"budgetProMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to set custom budget limits.\",\"budgetProTooltip\":\"Custom budget limits require ApX Pro.\",\"minTpsProMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to set custom minimum TPS targets.\",\"minTpsProTooltip\":\"Minimum per-user TPS targets require ApX Pro.\",\"clusterGatedAlert\":\"This multi-node configuration requires ApX Pro to apply to your workspace.\",\"clusterApplyProTooltip\":\"Applying multi-node configurations requires ApX Pro.\",\"upgradeToApplyCluster\":\"Upgrade to Apply Multi-Node\",\"hourlyCostTooltip\":\"Based on estimate hourly rental cost\",\"customVramProTooltip\":\"Custom VRAM above {max} GB requires ApX Pro.\",\"customVramProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate custom GPU memory capacities \u003e {max} GB for production workloads.\",\"customBandwidthProTooltip\":\"Custom memory bandwidth above {max} GB/s requires ApX Pro.\",\"customBandwidthProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate custom high-throughput hardware architectures \u003e {max} GB/s.\",\"customTflopsProTooltip\":\"Custom compute performance above {max} TFLOPS requires ApX Pro.\",\"customTflopsProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate high-performance compute hardware \u003e {max} TFLOPS.\",\"clusterLimitProInlineMessage\":\"Upgrade to \u003clink\u003eApX Pro\u003c/link\u003e to simulate larger clusters and higher parallelism levels.\",\"profileLimitTooltip\":\"Unlimited hardware profiles require ApX Pro.\",\"customModelLimitTooltip\":\"Saved custom model limit reached. ApX Pro required for unlimited custom model saves.\",\"gradientAccumulationProTooltip\":\"Gradient accumulation steps above {max} require ApX Pro.\",\"enableHighRangeSteps\":\"Enable High-Range Steps\",\"gradientAccumulationProTooltipMsg\":\"High-range gradient accumulation requires ApX Pro.\",\"deepspeedProTooltip\":\"ZeRO sharding presets require ApX Pro.\",\"optimizeTopologyProTooltip\":\"Optimize topology requires ApX Pro.\",\"gpusProTooltip\":\"Calculating setups with more than {max} GPUs requires ApX Pro.\",\"zeroShardingWarningTitle\":\"ZeRO Sharding Disabled\",\"zeroShardingWarningText\":\"Select a ZeRO stage to distribute memory across your GPUs.\",\"zeroShardingWarningTooltip\":\"Each GPU currently replicates the entire model and training states.\",\"zeroShardingEnableStage1\":\"Enable ZeRO-1\",\"zeroShardingEnableStage3\":\"Enable ZeRO-3\",\"numSamplesProTooltip\":\"Datasets larger than {max} samples require ApX Pro.\",\"tokensPerSampleProTooltip\":\"Sequence lengths above {max} tokens require ApX Pro.\",\"logScale\":\"Log Scale\",\"customInput\":\"Custom Input\",\"sliderInput\":\"Slider\",\"textInput\":\"Manual\",\"inputParameters\":\"Input Parameters\",\"datasetConfig\":{\"title\":\"Dataset Configuration\",\"tooltip\":\"Configure your training dataset size to estimate training time and costs\",\"description\":\"Define your dataset parameters to calculate total training time and token throughput\",\"numSamples\":\"Samples\",\"numSamplesDescription\":\"Number of training samples\",\"tokensPerSample\":\"Tokens/Sample\",\"tokensPerSampleDescription\":\"Avg tokens per sample\",\"numEpochs\":\"Epochs\",\"numEpochsDescription\":\"Training epochs\",\"totalTokens\":\"Total Tokens\",\"estimatedTime\":\"Est. Time\",\"timeMinutes\":\"{minutes}m\",\"timeHours\":\"{hours}h\",\"timeDays\":\"{days}d\"}},\"results\":{\"title\":\"Performance \u0026 VRAM Results\",\"gpuVramAllocation\":\"GPU VRAM Allocation\",\"unifiedMemoryAllocation\":\"Unified Memory Allocation\",\"offloadedMemoryBreakdownTitle\":\"Offloaded Memory Allocation ({target})\",\"viewMode\":{\"perGpu\":\"Per GPU\",\"total\":\"Total\",\"perNode\":\"Per Node\"},\"tabs\":{\"performance\":\"Performance\",\"energy\":\"Energy \u0026 Cost\"},\"unifiedMemory\":\"Unified Memory\",\"vram\":\"VRAM\",\"systemReserved\":\"System Reserved\",\"modelUsageTooltip\":\"Model Memory: {usage} GB ({percentage}%)\",\"systemReservedTooltip\":\"System Reserved (OS \u0026 hardware limit): {reserved} GB ({percentage}%)\",\"status\":{\"insufficient\":\"Insufficient\",\"very_high\":\"Very High\",\"high\":\"High\",\"moderate\":\"Moderate\",\"okay\":\"Okay\",\"ready\":\"Ready\",\"error\":\"Error\",\"mismatch\":\"Mismatch\"},\"autoFitToGpu\":\"Auto-Fit to GPU\",\"kvPoolExceededWarning\":\"KV pool saturation risk\",\"kvPoolExceededWarningDesc\":\"Concurrent requests exceed available KV cache blocks. Additional requests will experience queue latency or preemption.\",\"viewScalingChartLink\":\"View\",\"moeTooltip\":\"MoE Models: While only active experts compute per token, all experts often reside in VRAM for fast switching, contributing to total memory.\",\"ofTotal\":\"of {total} GB VRAM{numGpus, plural, =1 {} other { total across {numGpus} {deviceTypes}}}\",\"ofSingle\":\"of {total} GB VRAM{numGpus, plural, =1 {} other { per {deviceType}}}\",\"ofTotalUsable\":\"of {usable} GB usable ({total} GB total) VRAM{numGpus, plural, =1 {} other { total across {numGpus} {deviceTypes}}}\",\"ofSingleUsable\":\"of {usable} GB usable ({total} GB total) VRAM{numGpus, plural, =1 {} other { per {deviceType}}}\",\"deviceTypeSingular\":\"device\",\"deviceTypePlural\":\"devices\",\"gpuTypeSingular\":\"GPU\",\"gpuTypePlural\":\"GPUs\",\"sharedAndPerUser\":\"{shared} GB shared + {perUser} GB per user\",\"totalForUsers\":\"Total for {concurrentUsers} concurrent users\",\"perGpuForUsers\":\"Per GPU for {concurrentUsers} concurrent users\",\"generationSpeed\":\"Generation Speed: {speed}\",\"generationSpeedTooltip\":\"Estimated tokens generated per second when running a single sequence (higher is faster).\",\"latency\":\"(~{msPerToken} ms/token latency)\",\"ttft\":\"Time to First Token: {ttft}\",\"ttftTooltip\":\"Estimated time from submitting a prompt to re
16ceiving the first generated token. Includes prefill (processing input) and initial generation. Lower is better for perceived responsiveness.\",\"totalThroughput\":\"Total Throughput: {throughput}\",\"totalThroughputTooltip\":\"Maximum tokens generated per second across all {batchSize} batch slots (higher means more total processing power).\",\"perUserSpeed\":\"Per-User Speed: ~{perUserTps} tok/sec\",\"perUserSpeedTooltip\":\"With {concurrentUsers} concurrent users, each user experiences this average generation speed. {queueNote}\",\"queueNote\":\"Since users exceed batch size ({batchSize}), some requests wait in queue, reducing individual speed.\",\"cloudRentalCost\":\"Est. GPU Rental: {cost}\",\"cloudRentalCostTooltip\":\"Estimated cloud GPU rental cost per hour based on average market rates.\",\"localOnly\":\"N/A\",\"pcieBottleneckAlertDesc\":\"Tensor Parallelism over PCIe 4.0 x16 causes severe communication delays. Expect lower generation speeds.\",\"pipelineBubbleNoticeDesc\":\"Pipeline Parallelism increases latency for single requests due to sequential processing.\",\"weights\":\"Weights:\",\"kvCache\":\"KV Cache:\",\"attentionStructure\":\"Attention Structure:\",\"slidingWindow\":\"Sliding Window:\",\"linearAttention\":\"Linear Attention:\",\"attentionStructureTooltip\":\"{tooltip}\",\"positionalEmbeddings\":\"Positional Embeddings:\",\"layersRatio\":\"{ratio}% layers\",\"localWindow\":\"Local Window\",\"viewModelDetails\":\"View Model Specifications \u0026 Architecture\",\"method\":\"Method:\",\"basePrecision\":\"Base Precision:\",\"loraRank\":\"LoRA Rank:\",\"mode\":\"Mode: {mode}\",\"modeInference\":\"Inference\",\"modeFinetuning\":\"Fine-tuning ({method})\",\"batch\":\"Batch: {batchSize}\",\"devices\":\"Devices: {numGpus}\",\"gpus\":\"GPUs: {numGpus}\",\"deviceType\":\"devices\",\"gpuType\":\"GPUs\",\"users\":\"Users: {concurrentUsers}\",\"accumSteps\":\"Acc. Steps: {gradientAccumulationSteps}\",\"trainingSpeed\":\"Training Speed: {speed}\",\"trainingSpeedTooltip\":\"Estimated tokens processed per second during training (includes forward and backward passes).\",\"samplesPerSecond\":\"Samples/sec: ~{samples}\",\"samplesPerSecondTooltip\":\"Training samples processed per second with batch size of {batchSize}. Higher means faster training throughput.\",\"stepsPerSecond\":\"Steps/sec: ~{steps}\",\"stepsPerSecondTooltip\":\"Optimizer steps (weight updates) per second. With batch size {batchSize} and {gradientAccumulationSteps} accumulation steps, effective batch is {effectiveBatch} samples per step.\",\"totalTokens\":\"Total Tokens:\",\"estTrainingTime\":\"Est. Training Time:\",\"timeMinutes\":\"{minutes} min\",\"timeHours\":\"{hours} hrs\",\"timeDays\":\"{days} days\",\"proExportModal\":{\"title\":\"Download Report\",\"intro\":\"Download a PDF sizing report of the current configuration.\",\"tableHeaderSection\":\"Section\",\"tableHeaderDetails\":\"Details\",\"bulletParamsTitle\":\"Parameters\",\"bulletParamsDesc\":\"Hardware, model, and quantization specifications.\",\"bulletBreakdownTitle\":\"Memory Breakdown\",\"bulletBreakdownDesc\":\"Allocation for base weights, KV cache, and activation overhead.\",\"bulletPerformanceTitle\":\"Performance \u0026 Cost\",\"bulletPerformanceDesc\":\"Token throughput, latency, power consumption, and hosting cost estimates.\",\"ctaUpgrade\":\"Get Pro\",\"ctaSample\":\"Download Sample\",\"close\":\"Close\"}},\"memoryBreakdown\":{\"title\":\"Memory Allocation\"},\"offloadMemory\":{\"systemRam\":\"System RAM\",\"nvmeStorage\":\"NVMe Storage\",\"offloadedTo\":\"Offloaded to {storageType}:\",\"tooltip\":\"Amount of model data offloaded to {storageType} instead of using GPU VRAM\"},\"inferenceSimulation\":{\"title\":\"Inference Simulation\",\"weights\":\"Weights\",\"kvCache\":\"KV Cache\",\"on\":\"on\",\"customDevice\":\"Custom Device\",\"customGpu\":\"Custom GPU\",\"customAppleSilicon\":\"Custom Apple Silicon\",\"inputSeqLength\":\"Input sequence length: {seqLength} tokens\",\"simulateTTFT\":\"Simulate TTFT\",\"ttftTooltip\":\"Time To First Token: The delay before the first token is generated, including prompt processing and initialization\",\"pressPlay\":\"Press play to start inference\",\"configureToEnable\":\"Configure model and hardware to enable simulation\",\"tokenProgress\":\"{current} tokens · {time}s\"},\"costComparison\":{\"title\":\"GPU Training Cost Estimate\",\"hostingTitle\":\"GPU Hosting Cost Estimate\",\"tooltip\":\"Estimated costs for training this workload on cloud GPU rentals\",\"noPricingData\":\"Rental pricing data is not available for this GPU.\",\"checkProviders\":\"Rental pricing data is not available for this GPU. We recommend checking specific cloud providers for current rates.\",\"trainingDuration\":\"Training Duration\",\"hostingDuration\":\"Hosting Duration\",\"gpus\":\"GPUs\",\"estimatedCost\":\"Estimated Cloud Rental Cost by Provider Type\",\"tiers\":{\"budget\":\"Spot\",\"value\":\"On-Demand\"},\"durationNote\":\"For {time} training duration. Prices vary by provider.\",\"hostingDurationNote\":\"Estimated monthly hosting. Prices vary by provider.\",\"costBreakdown\":\"Cost Breakdown (Average)\",\"hourlyRate\"
16:\"Hourly Rate\",\"dailyCost\":\"Daily Cost (24hrs)\",\"monthlyCost\":\"Monthly Cost (730hrs)\",\"promoTitle\":\"Need More Compute or VRAM?\",\"promoDescription\":\"Scale your LLM training or inference instantly with on-demand cloud GPUs.\"},\"memoryLabels\":{\"draftModelWeights\":\"Draft Model Weights\",\"baseModelWeights\":\"Model Weights\",\"sharedWeights\":\"Shared Weights\",\"expertWeights\":\"Expert Weights\",\"expertWeightsAllLoaded\":\"Expert Weights (All Loaded)\",\"activationsEstimate\":\"Activations (Estimate)\",\"activations\":\"Activations\",\"kvCache\":\"KV Cache\",\"frameworkOverhead\":\"Framework Overhead\",\"multiGpuOverhead\":\"Multi-GPU Overhead\",\"multiDeviceOverhead\":\"Multi-Device Overhead\",\"optimizerStates\":\"Optimizer States\",\"gradients\":\"Gradients\",\"tempBuffers\":\"Temp Buffers\",\"loraAdapters\":\"LoRA Adapters, Optimizer \u0026 Gradients\",\"qloraBuffers\":\"QLoRA Buffers\",\"sharedBackboneWeights\":\"Shared Backbone Weights\",\"allExpertWeights\":\"All Expert Weights\",\"offloadedModelWeights\":\"Model Weights\",\"offloadedKvCache\":\"KV Cache\",\"offloadedActivations\":\"Activations\",\"offloadedWeights\":\"Weights\",\"offloadedOptimizerStates\":\"Optimizer States\",\"offloadedGradients\":\"Gradients\",\"logitsBuffer\":\"Logits Buffer\"},\"warnings\":{\"suboptimalParallelism\":\"Tensor Parallelism requires high-speed interconnects to scale efficiently. Running over {interconnect} will cause severe communication bottlenecks; Pipeline Parallelism is recommended instead.\",\"recommendTensorParallelism\":\"Recommendation: For {interconnect} interconnection, use \u003clink\u003eTensor Parallelism\u003c/link\u003e for optimal low-latency performance.\",\"highSeqLengthFinetuning\":\"Large sequence lengths require extremely large activation memory. Gradient checkpointing is recommended.\",\"noSetupFound\":\"No Setup Found\",\"noSetupFoundFinetuningLongContext\":\"Fine-tuning with long contexts requires massive activation memory. Try lowering the sequence length or reducing the batch size.\",\"noSetupFoundDefault\":\"No available hardware configuration fits the selected constraints. Try adjusting your parameters or constraints.\"},\"submitBenchmark\":{\"button\":\"Submit Benchmark\"},\"energy\":{\"title\":\"Energy \u0026 Infrastructure Cost\",\"tooltip\":\"Estimated power consumption, local electricity costs, and cloud compute rental rates.\",\"powerDraw\":\"Power Draw\",\"costPerHour\":\"Electricity Cost / Hour\",\"carbonPerDay\":\"COâ/day\",\"projections\":\"Electricity Cost Projections\",\"perDay\":\"Per Day\",\"perMonth\":\"Per Month\",\"perYear\":\"Per Year\",\"electricityCost\":\"Electricity Cost ($/kWh)\",\"electricityCostDescription\":\"Your local electricity rate per kilowatt-hour\",\"cloudRentalTitle\":\"Cloud Compute Cost\",\"cloudRentalPerHour\":\"Cloud Rental / Hour\",\"cloudRentalPerDay\":\"Per Day\",\"cloudRentalPerMonth\":\"Per Month\",\"cloudRentalPerYear\":\"Per Year\",\"localOnly\":\"N/A\",\"multiGpuNote\":\"Total daily energy consumption for {count} GPUs\",\"expandDetails\":\"Click to expand details\",\"regions\":{\"us\":\"US\",\"eu\":\"EU\",\"china\":\"China\"},\"environmentalImpact\":\"Environmental Impact\",\"carbonTooltip\":\"Carbon emissions are estimated based on your selected grid carbon intensity. Actual emissions vary by location and energy source.\",\"carbonPerYear\":\"Annual COâ Emissions\",\"carbonIntensity\":{\"label\":\"Grid Carbon Intensity\",\"description\":\"Select your region or energy source to calculate carbon emissions. This represents grams of COâ emitted per kilowatt-hour of electricity.\",\"regions\":\"By Region\",\"energySources\":\"By Energy Source\",\"globalAverage\":\"Global Average\",\"us\":\"United States\",\"eu\":\"European Union\",\"china\":\"China\",\"france\":\"France\",\"india\":\"India\",\"japan\":\"Japan\",\"brazil\":\"Brazil\",\"nuclear\":\"Nuclear\",\"wind\":\"Wind\",\"solar\":\"Solar\",\"hydro\":\"Hydro\",\"naturalGas\":\"Natural Gas\",\"coal\":\"Coal\"}},\"optimization\":{\"title\":\"Memory \u0026 Performance Optimization\",\"description\":\"Select a framework configuration to optimize training speed and memory efficie
16ncy\",\"badge\":\"Fine-Tuning Only\",\"selectPreset\":\"Select optimization preset\",\"customConfiguration\":\"ð ï¸ Custom Configuration\",\"enableCustom\":\"Enable custom to modify\",\"advancedConfiguration\":\"Advanced Configuration\",\"recommendation\":\"Recommendation\",\"recommendationText\":\"Based on your setup ({reason}), we recommend: {preset}\",\"applyRecommended\":\"Apply recommended preset â\",\"presets\":{\"unsloth\":{\"label\":\"Unsloth\",\"badge\":\"Fast \u0026 Efficient\",\"description\":\"Optimized for maximum speed and memory efficiency on single GPU. Best for LoRA/QLoRA.\",\"bestFor\":\"Best for:\",\"avoidFor\":\"Avoid for:\",\"recommended\":[\"Single GPU\",\"LoRA\",\"QLoRA\",\"Consumer GPUs\"],\"notRecommended\":[\"Multi-GPU (use ZeRO sharding)\",\"Full fine-tuning of 70B+\"]},\"deepspeed_zero2\":{\"label\":\"ZeRO-2\",\"badge\":\"Multi-GPU\",\"description\":\"Shards optimizer states and gradients. Good balance for multi-GPU setups. Compatible with DeepSpeed ZeRO-2 and PyTorch FSDP.\",\"bestFor\":\"Best for:\",\"avoidFor\":\"Avoid for:\",\"recommended\":[\"2-4 GPUs\",\"Full fine-tuning\",\"13B-70B models\"],\"notRecommended\":[\"Single GPU\",\"Very limited VRAM\"]},\"deepspeed_zero3\":{\"label\":\"ZeRO-3\",\"badge\":\"Max Memory\",\"description\":\"Maximum memory efficiency - shards optimizer states, gradients, and parameters. Compatible with DeepSpeed ZeRO-3 and PyTorch FSDP.\",\"bestFor\":\"Best for:\",\"avoidFor\":\"Avoid for:\",\"recommended\":[\"4+ GPUs\",\"70B+ models\",\"Limited VRAM per GPU\"],\"notRecommended\":[\"Single GPU\",\"Small models (\u003c7B)\",\"Speed-critical\"]},\"peft_minimal\":{\"label\":\"PEFT Minimal\",\"badge\":\"Lowest Memory\",\"description\":\"Ultra-efficient for tiny adapter training (LoRA/QLoRA). Minimal memory footprint.\",\"bestFor\":\"Best for:\",\"avoidFor\":\"Avoid for:\",\"recommended\":[\"Low VRAM\",\"LoRA rank ⤠32\",\"Quick iteration\"],\"notRecommended\":[\"Full fine-tuning\",\"Large batch sizes\"]},\"default\":{\"label\":\"Default\",\"badge\":\"Standard\",\"description\":\"Baseline configuration without special optimizations. Use for debugging or compatibility.\",\"bestFor\":\"Best for:\",\"avoidFor\":\"Avoid for:\",\"recommended\":[\"Debugging\",\"Compatibility\",\"Testing\"],\"notRecommended\":[\"Production training\",\"Limited resources\"]},\"custom\":{\"label\":\"Custom Configuration\",\"badge\":\"Manual\",\"description\":\"Manually configure individual optimizations to suit your specific needs.\"}},\"customPresetLabel\":\"Custom\",\"optimizations\":{\"flashAttention\":\"Flash Attention\",\"flashAttentionTooltip\":\"Fused attention kernel that reduces memory usage and speeds up training.\",\"gradientCheckpointing\":\"Gradient Checkpointing\",\"gradientCheckpointingTooltip\":\"Trades compute for memory by recalculating activations during the backward pass to reduce memory usage.\",\"checkpointRatio\":\"Checkpoint Ratio: {ratio}%\",\"use8bitOptimizer\":\"8-bit Optimizer\",\"use8bitOptimizerTooltip\":\"Reduces optimizer memory usage with minimal impact on training quality.\",\"pagedOptimizer\":\"Paged Optimizer\",\"pagedOptimizerTooltip\":\"Automatically offloads optimizer states to CPU RAM when GPU memory is exceeded.\",\"fusedKernels\":\"Fused Kernels\",\"fusedKernelsTooltip\":\"Optimized CUDA kernels for operators like RMSNorm and RoPE to speed up training.\",\"sequencePacking\":\"Sequence Packing\",\"sequencePackingTooltip\":\"Packs multiple training samples into a single sequence to speed up training.\",\"dynamicPadding\":\"Dynamic Padding\",\"dynamicPaddingTooltip\":\"Pads samples to the maximum length within the batch instead of the global maximum length.\",\"useBf16True\":\"Pure 16-bit Training\",\"useBf16TrueTooltip\":\"Runs optimizer states in 16-bit instead of FP32. Saves optimizer memory but can affect training stability.\",\"activationOffloading\":\"Activation Offloading\",\"activationOffloadingTooltip\":\"Offloads activation tensors to CPU RAM during the forward pass to reduce VRAM usage.\",\"optimizerType\":\"Optimizer Type\",\"optimizerTypeDescription\":\"Algorithm for updating model weights during training. AdamW is recommended for most cases.\",\"optimizerTypes\":{\"adamw\":\"AdamW (Recommended)\",\"adam\":\"Adam\",\"sgd\":\"SGD (Lowest Memory)\",\"adafactor\":\"Adafactor (Memory Efficient)\"},\"zeroStage\":\"ZeRO Stage\",\"zeroStageDescription\":\"Higher stages reduce memory per GPU but increase communication overhead\",\"zeroStageOptions\":{\"0\":\"Stage 0 - No Sharding\",\"1\":\"Stage 1 - Optimizer State Sharding\",\"2\":\"Stage 2 - Optimizer + Gradient Sharding\",\"3\":\"Stage 3 - Full Model Sharding\"}},\"activeOptimizations\":\"Active Optimizations\",\"advanced\":{\"title\":\"Advanced Configuration\"}},\"runtime\":{\"runtimeLabel\":\"Runtime\",\"frameworkLabel\":\"Framework\",\"auto\":\"Auto\",\"autoResolved\":\"Auto ({name})\",\"modifiedName\":\"{name} · modified\",\"autoInference\":\"Picks the most common runtime for your setup: llama.cpp for GGUF quantizations, vLLM otherwise.\",\"autoFinetuning\":\"Picks HF Trainer, the most common fine-tuning setup.\",\"resetDefaults\":\"Reset to default\",\"presetDefault\":\"Default\",\"changed\":\"changed\",\"resetControl\":\"Back to the preset default\",\"noControl
16s\":\"{name} has no adjustable runtime settings.\",\"estimatedFor\":\"Estimated for {name}\",\"estimatedForAuto\":\"Estimated for {name} (auto)\",\"change\":\"Change\",\"presets\":{\"generic\":\"Framework-agnostic estimate blended across runtimes, as in earlier versions of this calculator.\",\"vllm\":\"Serving engine: reserves most of the GPU for a paged KV cache, CUDA graphs, chunked prefill, continuous batching, prefix caching.\",\"sglang\":\"Serving engine: RadixAttention tree-structured prefix caching, multi-layer memory pool, chunked prefill, continuous batching.\",\"tensorrt_llm\":\"Compiled serving engine: reserves free GPU memory for a paged KV cache, in-flight batching, KV block reuse.\",\"llama_cpp\":\"Local runtime for GGUF models: allocates weights, KV cache and compute buffers at load; small overhead.\",\"ollama\":\"Local runtime built on llama.cpp, with its own scheduling and memory defaults.\",\"hf_transformers\":\"Plain PyTorch: eager execution, no KV paging. Common for research and bitsandbytes models.\",\"hf_trainer\":\"Hugging Face Trainer / TRL with PEFT, with their default settings.\",\"unsloth\":\"Fused kernels, checkpointing offloaded to host memory and chunked loss: much less memory, faster on one GPU.\",\"torchtune\":\"PyTorch-native recipes: activation checkpointing, linear cross-entropy loss, pure bf16 weights and optimizer states.\"},\"controls\":{\"execution\":{\"label\":\"Execution\",\"tooltip\":\"Captured: each decode step replays a CUDA graph or compiled engine, so kernel launch cost is paid once. Eager: kernels are launched one by one from Python every step.\",\"options\":{\"captured\":\"Graph captured\",\"eager\":\"Dynamic eager\"}},\"prefill\":{\"label\":\"Prefill\",\"tooltip\":\"How the prompt is processed. Chunked prefill bounds activation memory by the chunk size; a whole-prompt pass holds activations for the full prompt.\",\"options\":{\"chunked\":\"Chunks of {count} tokens\",\"full\":\"Whole prompt at once\"}},\"kv_pool\":{\"label\":\"GPU memory reserved\",\"tooltip\":\"Share of GPU memory the engine claims at startup. Whatever the weights and buffers leave is the KV cache pool, which decides how many tokens can be in flight.\"},\"prefix_caching\":{\"label\":\"Prefix caching\",\"tooltip\":\"Requests that share a prompt prefix reuse its cached KV blocks. How much is shared is set by the shared prompt prefix under Advanced Configuration.\"},\"batching\":{\"label\":\"Continuous batching\",\"tooltip\":\"Requests join and leave the batch every step instead of waiting for the whole batch to finish.\"},\"preallocation\":{\"label\":\"Static VRAM pre-allocation\",\"tooltip\":\"Reserve a share of GPU memory up front; the slack becomes the KV cache pool.\"}},\"autoSelection\":\"Selection: {name}\",\"runtimeSectionTitle\":\"Runtime settings\"},\"sizingGuide\":{\"title\":\"Calculator Guide\",\"sidebarBadge\":\"Guide\",\"cardTitle\":\"Calculator Guide\",\"cardDescription\":\"Understand concepts for sizing transformer memory, KV cache scaling, and roofline throughput.\\n\\nLearn how each parameter in this tool affects real-world VRAM consumption and serving speed.\",\"button\":\"Explore Guide\",\"description\":\"Learn the concepts\",\"learnMore\":\"Learn more\",\"kvCacheTooltip\":\"KV cache memory scales with context length, layers, and attention topology.\",\"parallelismGuideLink\":\"Guide to distributed serving strategies\",\"rooflineGuideLink\":\"Learn roofline performance modeling\"},\"methodology\":{\"title\":\"Calculation Methodology\",\"description\":\"How it is estimated\"},\"scorecard\":{\"title\":\"Accuracy Scorecard\",\"description\":\"Ground-truth calibration\",\"sidebarDescription\":\"{passRate}% in-band ({error}% error)\",\"parameterNote\":\"Memory predictions are highly deterministic and very accurate, while speed estimates are more sensitive to runtime and hardware configurations. Misses are largely due to unspecified parameter data in the source benchmarks.\",\"close\":\"Close\",\"summary\":{\"title\":\"Calibration overview\",\"description\":\"Comparing predictions against verified ground-truth configurations.\"}
16,\"tabs\":{\"memory\":\"VRAM Usage\",\"inferenceSpeed\":\"Inference TPS\",\"finetuningSpeed\":\"Fine-Tuning TPS\"},\"filters\":{\"title\":\"Filters\",\"reset\":\"Reset filters\",\"searchLabel\":\"Search\",\"searchPlaceholder\":\"Search ID, model, hardware...\",\"suite\":\"Suite\",\"gpuHardware\":\"GPU Hardware\",\"allHardware\":\"All hardware\",\"architecture\":\"Architecture\",\"allArchitectures\":\"All\",\"dense\":\"Dense\",\"moe\":\"MoE\",\"sourceType\":\"Source type\",\"allSources\":\"All\",\"internal\":\"Internal\",\"external\":\"Published\",\"status\":\"Status\",\"allStatuses\":\"All\",\"showing\":\"Showing {count} of {total} cases\",\"activeFilters\":\"Active filters:\"},\"actions\":{\"goToCalculator\":\"Open calculator\",\"openInCalculator\":\"Open in calculator\",\"verifyInCalculator\":\"Open in calculator\",\"viewSource\":\"View source benchmark\"},\"badges\":{\"all\":\"All\",\"inference\":\"Inference\",\"finetuning\":\"Fine-Tuning\"},\"categories\":{\"title\":\"Evaluation Categories\",\"description\":\"Prefixes indicate the targeted component and verification standard.\",\"headers\":{\"prefix\":\"Prefix\",\"category\":\"Category\",\"metric\":\"Target Metric\",\"nature\":\"Validation Standard\"},\"rows\":{\"a\":{\"prefix\":\"A\",\"category\":\"Model Weights\",\"metric\":\"Base \u0026 Expert Weights\",\"nature\":\"Published model weight artifact file sizes (.safetensors / GGUF) on disk.\"},\"b\":{\"prefix\":\"B\",\"category\":\"KV Cache\",\"metric\":\"KV Cache\",\"nature\":\"Exact tensor geometry derived from published layer counts, KV heads, and dimensions.\"},\"d\":{\"prefix\":\"D\",\"category\":\"Fine-Tuning\",\"metric\":\"VRAM Usage\",\"nature\":\"Published training recipes (ZeRO-3, LoRA, QLoRA) and profiler benchmarks.\"},\"e\":{\"prefix\":\"E\",\"category\":\"End-to-End Inference\",\"metric\":\"VRAM Usage\",\"nature\":\"Physical hardware profiler measurements including CUDA context and runtime overheads.\"},\"c\":{\"prefix\":\"C / N\",\"category\":\"Decode Speed\",\"metric\":\"Decode Speed (tok/s)\",\"nature\":\"Single-stream text generation speed on Apple Silicon and datacenter GPUs.\"},\"t\":{\"prefix\":\"T\",\"category\":\"Training Speed\",\"metric\":\"Training Speed (tok/s)\",\"nature\":\"Fine-tuning throughput across torchtune, HF FSDP, DeepSpeed ZeRO-3, and NeMo benchmarks.\"}}},\"table\":{\"id\":\"ID\",\"description\":\"Scenario \u0026 Config\",\"expected\":\"Measured\",\"predicted\":\"Predicted\",\"status\":\"Status\",\"ratio\":\"Ratio\",\"source\":\"Source\"},\"metrics\":{\"inBand\":\"In-Band Pass Rate\",\"medianError\":\"Median Error\",\"gmfe\":\"GMFE\",\"bias\":\"Systemic Bias\",\"loading\":\"Calculating scorecard predictions...\",\"notRun\":\"N/A\",\"pass\":\"Pass\",\"miss\":\"Miss\",\"overpredicts\":\"Overpredicts\",\"slightOverpredict\":\"Slight Overpredict\",\"underpredicts\":\"Underpredicts\",\"slightUnderpredict\":\"Slight Underpredict\",\"centered\":\"Centered\",\"inBandDescription\":\"Predictions matching target boundaries\",\"medianErrorDescription\":\"Typical absolute prediction error\",\"gmfeDescription\":\"Geometric mean error factor\",\"biasDescription\":\"Overall calibration offset direction\",\"labels\":{\"vramUsage\":\"VRAM Usage\",\"baseModelWeights\":\"Base Model Weights\",\"expertWeights\":\"Expert Weights\",\"kvCache\":\"KV Cache\",\"decodeSpeed\":\"Decode Speed\",\"throughputSpeed\":\"Throughput Speed\",\"trainingSpeed\":\"Training Speed\"}},\"details\":{\"modelSizeArch\":\"Model Size / Architecture\",\"gpuConfig\":\"GPU Configuration\",\"quantization\":\"Quantization\",\"kvCacheQuantization\":\"KV Cache Quantization\",\"seqLength\":\"Sequence Length\",\"batchSize\":\"Batch Size\",\"concurrentUsers\":\"Concurrent Users\",\"prefillMode\":\"Prefill Mode\",\"executionMode\":\"Execution Mode\",\"inferenceParallelism\":\"Inference Parallelism\",\"loraRank\":\"LoRA Rank\",\"gradientAccumulationSteps\":\"Gradient Accumulation Steps\",\"offloading\":\"Offloading\",\"speculativeDecoding\":\"Speculative Decoding\",\"gpuMemoryUtilization\":\"GPU Memory Utilization\",\"modeMethod\":\"Mode / Method\",\"parallelismDegrees\":\"Parallelism Degrees\",\"interconnect\":\"Interconnect\",\"optimizationFeatures\":\"Optimization Features\",\"inputParameters\":\"Input Parameters\",\"enabled\":\"Enabled\",\"none\":\"None\"}},\"faq\":{\"title\":\"Frequently Asked Questions\",\"description\":\"How accurate is this?\",\"accuracy\":{\"question\":\"How accurate is this calculator?\",\"answer1\":\"It is highly accurate because it directly models core architectural components, including parameter footprints, attention mechanisms, dynamic KV cache growth, and distributed parallelism. This precision is essential because small calculation discrepancies compound quickly across multi-GPU clusters, leading to provisioning errors, unexpected crashes, and performance degradation.\",\"answer2\":\"Calculations are continuously validated against physical GPU benchmarks from verified hardware runs and established production frameworks, with all automated evaluation test suites and measured error rates published on the \u003cscorec
16ardLink\u003eAccuracy Scorecard\u003c/scorecardLink\u003e. If you notice a variance between the estimate and your physical deployment, submit it through the \u003cfeedbackLink\u003efeedback\u003c/feedbackLink\u003e form so we can check the configuration.\"},\"ollama\":{\"question\":\"Why is the VRAM requirement higher here than when I run a model on Ollama?\",\"answer\":\"Ollama pulls 4-bit quantized weights by default to fit on consumer hardware, whereas this calculator defaults to uncompressed 16-bit precision standard in benchmarks and production deployments. Selecting a 4-bit option in the Quantization dropdown will align the estimate with your Ollama setup.\"},\"moe\":{\"question\":\"Why do MoE models use so much VRAM?\",\"answer\":\"Although only a fraction of experts are active per token, all expert weights must reside in VRAM to enable zero-latency routing. MoE architectures reduce compute work rather than resident memory, delivering higher generation speeds than dense models of identical footprint. If an MoE model appears to use less VRAM in practice, it is typically due to aggressive quantization rather than the MoE architecture itself.\",\"references\":\"References:\",\"ref1\":{\"title\":\"Mixture of Lookup Experts\",\"authors\":\" by Shibo Jie, Yehui Tang, Kai Han, et al. (2025)\"},\"ref2\":{\"title\":\"Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques\",\"authors\":\" by Shwai He, Daize Dong, Liang Ding, Ang Li (2024)\"},\"ref3\":{\"title\":\"Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient*\",\"authors\":\" by Jan Ludziejewski, Maciej Pióro, Jakub Krajewski, et al. (2025)\",\"note\":\"(*Note: The memory efficiency discussed in this paper is achieved through specific techniques and optimizations, not from the MoE architecture itself.)\"}},\"tps\":{\"question\":\"How is the TPS (Tokens per Second) calculated?\",\"answer\":\"TPS and latency are calculated using a physical memory-bandwidth roofline model based on GPU-specific memory bandwidth and FLOPS. The prefill phase is estimated from the compute work required relative to hardware FLOPS, while the decode phase is calculated from memory read time for model weights and KV cache. The model accounts for multi-GPU scaling under tensor and pipeline parallelism, interconnect bottlenecks, batch size saturation curves, and expert thrashing in Mixture of Experts architectures.\"},\"tflops\":{\"question\":\"Why does the TFLOPS value differ from official GPU specifications?\",\"answer\":\"GPU specifications often list either traditional FP32 compute performance or sparse FP16 Tensor Core performance. Traditional FP32 performance is too slow to model LLM execution, which runs in half-precision or lower. Sparse FP16 specs assume hardware-level 2:4 sparsity, which standard LLM runners do not utilize by default. This calculator uses peak dense FP16/BF16 Tensor Core, AMD Matrix Core, or Apple Silicon native FP16 TFLOPS to ensure accurate speed predictions.\"},\"proFeatures\":{\"question\":\"Why are some features restricted to ApX Pro?\",\"answer1\":\"The standard calculator provides memory and throughput modeling for standard deployments, open-weight models, and GPU cluster topologies.\",\"answer2\":\"ApX Pro is designed for users evaluating production-scale workloads that exceed standard boundaries. It supports high concurrency and batch sizing, continuous batching profiling, full parameter sharding, and high-speed fabrics.\"}},\"courseCta\":{\"title\":\"Getting Started with Local LLMs\",\"description\":\"If you're setting up local hardware for the first time, our free guide walks through the installation and configuration steps.\",\"button\":\"Read Guide\"},\"clusterTopology\":{\"title\":\"Cluster Topology\",\"invalid\":\"Mismatch\",\"suboptimal\":\"Suboptimal\",\"optimizeTooltip\":\"Automatically balance Tensor Parallelism (TP) and Pipeline Parallelism (PP) based on cluster interconnect speeds and memory size to fit the model efficiently.\",\"optimizeButton\":\"Optimize\",\"totalGpus\":\"Total: {total} GPUs\",\"grid3d\":\"3D Grid: {tp}TP à {pp}PP à {dp}DP\",\"validationError\":\"Configuration mismatch: TP à PP ({tpXpp}) must divide total GPUs ({totalGpus}) evenly. DP is currently mismatched.\",\"suboptimalWarning\":\"Suboptimal: TP degree ({tpDegree}) exceeds GPUs per node ({gpusPerNode}). Splitting TP across physical nodes introduces inter-node network bottlenecks.\",\"nodeLabel\":\"Node {index}\",\"chassisLabel\":\"Chassis #{index}\",\"gpuTooltip\":\"GPU #{gpuIdx} | DP replica {dpIdx} · PP stage {ppIdx} · TP position {tpIdx}\",\"pipelineFlowLabel\":\"Pipeline Stages (PP) Sequential Flow:\",\"stageLabel\":\"Stage {index}\",\"concise\":\"Concise\",\"full\":\"Full Topology\",\"autoFix\":\"Auto-Fix\",\"interNodeStatusLabel\":\"Inter-Node: {interconnect}\",\"networkBottleneckDesc\":\"Tensor Parallel is split across nodes over standard networks. Expect major latency.\",\"tpSuboptimalDesc\":\"TP crosses nodes, but high-speed interconnect mitigates some latency.\",\"networkOptimalDesc\":\"Optimized high-speed network. Fully scales Data \u0026 Pipeline Parallelism.\",\"networkStandardDesc\":\"Standard network. Best suited for Pipeline Parallelism execution stages.\",\"statusBottleneck\":\"Bottleneck\",\"statusOptimal\":\"Optimal\",\"statusStandard\":\"Standard\",\"hardwareTop
16ology\":\"Hardware Topology\",\"tensorParallel\":\"Tensor Parallel ({tp}x)\",\"pipelineParallel\":\"Pipeline Parallel ({pp}x)\",\"dataParallel\":\"Data Parallel ({dp}x)\",\"layoutView\":\"Layout View\",\"resolve\":\"Resolve\",\"identicalNodes\":\"+ {count} Identical Nodes\",\"moreIdenticalNodes\":\"+ {count} more identical nodes\",\"additionalGpus\":\"({count} additional {gpu} GPUs)\",\"connectedVia\":\"Connected via {interconnect}\",\"localRig\":\"Local Rig\",\"interconnectBus\":\"Interconnect \u0026 Communication Bus\",\"intraNodeLabel\":\"Intra-Node: {interconnect}\",\"intraNodeBottleneckDesc\":\"Tensor Parallelism over PCIe causes significant communication latency.\",\"intraNodeOptimalDesc\":\"High-speed GPU bus. Fully supports low-latency model parallelism.\",\"intraNodeStandardDesc\":\"PCIe connection. Recommended for Pipeline or Data Parallelism.\",\"stagesCount\":\"... {count} stages ...\"},\"planner\":{\"none\":\"None\",\"setupPrefix\":\"Setup:\",\"recommend\":\"Recommend\",\"optimize\":\"Optimize\",\"recommendTooltip\":\"Find setup to run this model\",\"optimizeTooltip\":\"Optimize model for this setup\",\"recommendSetup\":\"Recommend Setup\",\"optimizeConfig\":\"Auto-Tune\",\"autoFitToGpu\":\"Auto-Fit to GPU\",\"optimizerModalTitle\":\"Auto-Tune\",\"optimizerModalSubtitle\":\"Optimize runtime parameters for your hardware\",\"planTitleInference\":\"Find setup to run {model}\",\"planTitleFinetuning\":\"Find setup to fine-tune {model}\",\"optimizerTitleInference\":\"Optimize {model} to run on {gpu}\",\"optimizerTitleFinetuning\":\"Optimize {model} to fine-tune on {gpu}\",\"targetModel\":\"Target Model\",\"currentSetup\":\"Current Setup\",\"optimizationGoal\":\"Optimization Goal\",\"goalAutoFit\":\"Auto-Fit\",\"goalAutoFitDesc\":\"Minimal parameter reduction to resolve memory limits\",\"goalMaxQuality\":\"Best Quality\",\"goalMaxQualityDesc\":\"Highest precision that fits within memory\",\"goalMaxContext\":\"Max Context\",\"goalMaxContextDesc\":\"Largest context length supported by this hardware\",\"goalMaxThroughput\":\"Max Throughput\",\"goalMaxThroughputDesc\":\"Optimal batch size and precision for peak tokens per second\",\"applyConfig\":\"Apply Configuration\",\"appliedConfig\":\"Applied\",\"evaluatingConfigs\":\"Evaluating configurations...\",\"noFeasibleConfig\":\"Model cannot feasibly fit on the selected GPU.\",\"noFeasibleAdvice\":\"This model exceeds the memory capacity of the selected GPU. Select higher-capacity hardware or add GPUs.\",\"noFeasibleHardwareCapacity\":\"This model exceeds the memory capacity of the selected GPU. Select higher-capacity hardware or add GPUs.\",\"noFeasibleQuantRestricted\":\"No selected precision fits in memory. Enable lower precision formats like 4-bit to fit this model on your hardware.\",\"noFeasibleSlaRestricted\":\"Configuration fits in memory, but does not meet your minimum speed or latency constraints. Relax performance constraints or select faster hardware.\",\"findCompatibleHardware\":\"Find Compatible Hardware\",\"vramHeadroom\":\"Headroom\",\"tokensPerSec\":\"Tokens / Sec\",\"seqLength\":\"Context Length\",\"batchSize\":\"Batch Size\",\"precision\":\"Precision\",\"kvCache\":\"KV Cache\",\"higherCost\":\"{pct}% higher cost\",\"lowerCost\":\"{pct}% lower cost\",\"sameCost\":\"Same cost\",\"moreThroughput\":\"{pct}% more throughput\",\"lowerThroughput\":\"{pct}% lower throughput\",\"equalThroughput\":\"Equal throughput\",\"planConfig\":\"Recommend Setup\",\"planSubtitle\":\"Find hardware configurations to run your model\",\"performanceConstraints\":\"Performance Constraints\",\"concurrentUsers\":\"Concurrent Users:\",\"logScale\":\"Log Scale\",\"hardwareType\":\"Hardware Type:\",\"selectHardwareType\":\"Select Hardware Type\",\"precisionQuantization\":\"Precision (Quantization):\",\"precisionFixed\":\"Precision is fixed for {method}.\",\"slaBudgetConstraints\":\"SLA \u0026 Budget Constraints\",\"slaDescription\":\"Filter configurations by latency targets, hourly cost limits, or minimum speeds.\",\"maxTtft\":\"Max TTFT Latency Target:\",\"maxBudget\":\"Max Hourly Budget:\",\"minTps\":\"Min Per-User TPS:\",\"minTrainingSpeed\":\"Min Training Speed:\",\"minTrainingSpeedTooltip\":\"Upgrade to ApX Pro to set a minimum training speed target\",\"cheapestSetup\":\"Lowest Cost\",\"maxThroughput\":\"Max Throughput\",\"evaluatedConfigs\":\"Evaluated {count} configurations\",\"vramUsed\":\"VRAM Used\",\"cheapest\":\"Lowest Cost\",\"fastest\":\"Fastest\",\"recommended\":\"Recommended\",\"localHardware\":\"Local Hardware\",\"apply\":\"Apply\",\"trainingSpeed\":\"Training Speed\",\"samplesSec\":\"Samples / Sec\",\"stepsSec\":\"Steps / Sec\",\"singleStreamSpeed\":\"Single-Stream Speed\",\"aggregateThroughput\":\"Aggregate Throughput\",\"latencyTtft\":\"Latency (TTFT)\",\"vramAllocation\":\"VRAM Allocation\",\"proPlanRequired\":\"Pro Plan Required\",\"proPlanDescription\":\"This model configuration requires Pro features (such as multi-GPU scaling, Tensor/Pipeline Parallelism, or datacenter GPUs). We evaluated {count} configurations, but they are restricted to Pro plans.\",\"upgradeToPro\":\"Upgrade to Pro\",\"upgrade\":\"Upgrade\",\"alternativeConfigs\":\"Alternative Hardware Configurations\",\"hourlyCost\":\"Hourly Cost\",\"speedLatency\":\"Speed \u0026 Latency\",\"vsRecommended\":\"vs Recommended\",\"showFewer\":\"Show fewer configurations\",\"showAll\":\"Show all {count} alternatives\",\"noAlternatives\":\"No alternative configurations match the selected constraints.\",\"eliminationLog\":\"Elimination Log ({count}
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Finetune on {gpuLabel}\",\"dynamicDescriptionFinetune\":\"Open-weight models you can tune with {method} on your {gpuLabel}.\",\"dynamicTitleOpenWeight\":\"Open-Weight LLMs for {gpuLabel}\",\"dynamicTitleOpenWeightTop\":\"Top Open-Weight LLMs for {gpuLabel}\",\"dynamicTitleOpenWeightLatest\":\"Latest Open-Weight LLMs for {gpuLabel}\",\"dynamicDescriptionOpenWeight\":\"A comprehensive list of open-weight LLMs filtered for your hardware configuration.\",\"dynamicTitleLatestReleased\":\"Latest LLMs Released\",\"dynamicDescriptionLatestReleased\":\"Browse the newest language models as soon as they are released.\",\"dynamicTitleTopLLMs\":\"Top LLMs\",\"dynamicDescriptionTopLLMs\":\"Explore the best-performing LLMs, ranked by performance and capabilities.\"},\"LLMVariantInfo\":{\"rankings\":\"Rankings\",\"modelsBreadcrumb\":\"Models\",\"activeParameters\":\"Active Parameters\",\"parameters\":\"Parameters\",\"contextLength\":\"Context Length\",\"modality\":\"Modality\",\"architecture\":\"Architecture\",\"license\":\"License\",\"releaseDate\":\"Release Date\",\"knowledgeCutoff\":\"Knowledge Cutoff\",\"apiPricing\":\"API Pricing (per 1M)\",\"pricingSelfHostedOnly\":\"Self-hosted only\",\"pricingInputOutput\":\"Input: {input} · Output: {output}\",\"parametersUndisclosed\":\"Undisclosed\",\"parametersEstimated\":\"{value} (Estimated)\",\"proprietaryLicense\":\"Proprietary\",\"technicalSpecifications\":\"Technical Specifications\",\"technicalSpecsLimited\":\"Architecture specifications are undisclosed for proprietary models.\",\"attentionStructure\":\"Attention Structure\",\"hiddenDimensionSize\":\"Hidden Dimension Size\",\"numberOfLayers\":\"Number of Layers\",\"attentionHeads\":\"Attention Heads\",\"keyValueHeads\":\"Key-Value Heads\",\"activationFunction\":\"Activation Function\",\"normalization\":\"Normalization\",\"positionEmbedding\":\"Position Embedding\",\"totalExpertParameters\":\"Total Expert Parameters\",\"auxiliaryParameters\":\"Auxiliary Parameters\",\"numberOfExperts\":\"Number of Experts\",\"activeExperts\":\"Active Experts\",\"ffnIntermediateSize\":\"FFN Intermediate Size (Dense)\",\"moeFfnIntermediateSize\":\"FFN Intermediate Size (per Expert)\",\"numSharedExperts\":\"Shared Experts\",\"numDenseLayers\":\"Dense Layers Before MoE\",\"numMtpLayers\":\"Multi-Token Prediction Heads\",\"attentionHeadDim\":\"Attention Head Dimension\",\"usesSlidingWindow\":\"Sliding Window Attention\",\"slidingWindowSize\":\"Sliding Window Size\",\"slidingWindowRatio\":\"Sliding Window Ratio\",\"usesLinearAttention\":\"Linear Attention\",\"linearAttentionRatio\":\"Linear Attention Ratio\",\"kvSourceLayerCount\":\"KV Source Layers\",\"kvLatentDim\":\"KV Latent Dimension\",\"indexerDim\":\"Indexer Key Dimension\",\"ropeTheta\":\"RoPE Theta\",\"vocabSize\":\"Vocabulary Size\",\"groupTransformer\":\"Attention\",\"groupDimensions\":\"Dimensions\",\"groupTokenizer\":\"Tokenizer\",\"groupMoE\":\"Mixture of Experts\",\"openSource\":\"Open Source\",\"closedSource\":\"Closed Source\",\"openWeights\":\"Open Weights\",\"closedWeights\":\"Closed Weights\",\"comparing\":\"Comparing\",\"openPage\":\"(open page)\",\"architectureDiagram\":\"Architecture Diagram\",\"proprietaryArchitecture\":\"Proprietary Architecture\",\"memoryUsageTitle\":\"Memory Usage By Context Length\",\"vramChartDescription\":\"Estimated VRAM requirement as context length grows, up to the model's maximum of {maxContext}K tokens.\",\"vramChartDescriptionNoContext\":\"Estimated VRAM requirement as context length grows.\",\"dashedLineShows\":\"Dashed line shows\",\"officialDocumentation\":\"Official Documentation\",\"releaseNotes\":\"Release Notes\",\"readThePaper\":\"Read the Paper\",\"downloadWeights\":\"Download Weights\",\"sourceCode\":\"Source Code\",\"aboutModel\":\"About {name}\",\"aboutFamily\":\"About {familyName}\",\"otherModels\":\"Other {familyName} Models\",\"noRelatedModels\":\"No related models available\",\"overallRank\":\"Overall Rank\",\"codingRank\":\"Coding Rank\",\"gpuRequirements\":\"GPU Requirements\",\"fullCalculator\":\"Full Calculator\",\"quantization\":\"Quantization\",\"quantizationDescription\":\"Choose the quantization method for model weights\",\"contextSizeTokens\":\"Context Size: {count, plural, one {# token} other {# tokens}}\",\"vramRequired\":\"VRAM Required\",\"recommendedGpus\":\"Recommended GPUs\",\"totalVram\":\"Total: {total}GB VRAM\",\"planMultiGpu\":\"Plan Multi-GPU Rig\",\"planCluster\":\"Configure Cluster Serving\",\"selectOptions\":\"Select quantization and context size to see GPU recommendations\",\"resources\":\"Resources\",\"systemRequirementsTitle\":\"System Requirements\",\"systemRequirementsDescription\":\"VRAM requirements for different quantization methods and context sizes\",\"contextSize\":\"Context Size\",\"recommendedGpuSetup\":\"GPU Setup\",\"tokens\":\"{count, plural, one {# token} other {# tokens}}\",\"gpuCategories\":{\"consumer\":\"Consumer\",\"datacenter\":\"Datacenter\",\"appleSilicon\":\"Apple Silicon\"},\"benchmarks\":{\"title\":\"Evaluation Benchmarks\",\"rank\":\"Rank\",\"benchmark\":\"Benchmark\",\"score\":\"Score\",\"relativeScoreTooltip\":\"This is a relative score, not a percentage. Higher is better.\",\"percentageScoreTooltip\":\"This score is on a scale of 0.0 to 1.0. Higher is better.\",\"thinkingLevelTooltip\":\"Thinking Level\",\"standardThinkingLevel\":\"Standard\",\"noBenchmarks\":\"No evaluation benchmarks for {modelName} available.\",\"archivedBadge\":\"Archived\",\"archivedTooltip\":\"Benchmark is no longer updated. Excluded from composite ranking.\",\"referenceBadge\":\"Reference\",\"referenceTooltip\":\"Reference only. Excluded from composite ranking.\",\"evalScopes\":{\"livebench_overall\":\"General\",\"livebench_coding\":\"Coding\",\"livebench_reasoning\":\"Reasoning\",\"livebench_agentic\":\"Agentic Coding\",\"livebench_mathematics\":\"Mathematics\",\"livebench_data_analysis\":\"Data Analysis\",\"livebench_language\":\"Language\",\"livebench_average\":\"LiveBench Average\",\"aider_coding\":\"Coding\",\"aider_refactoring\":\"Refactoring\",\"prollm_stackunseen\":\"StackUnseen\",\"prollm_stackeval\":\"StackEval\",\"prollm_qa_assistant\":\"QA Assistant\",\"prollm_summarization\":\"Summarization\",\"webdev_arena\":\"Web Development\",\"agent_arena\":\"Agent Arena\",\"text_arena\":\"General Text\",\"mmlu\":\"General Knowledge\",\"mmlu_pro\":\"Professional Knowledge\",\"gpqa\":\"Graduate-Level QA\",\"swe_bench_verified\":\"Software Engineering\",\"artificial_analysis_intelligence\":\"Intelligence Index\",\"artificial_analysis_coding\":\"Coding Index\",\"artificial_analysis_agentic\":\"Agentic Index\"}},\"transparency\":{\"title\":\"Model Integrity\",\"totalScore\":\"Total Score\",\"outOf\":\"out of 100\",\"pillars\":\"Transparency Pillars\",\"upstream\":\"Upstream\",\"model\":\"Model\",\"downstream\":\"Downstream\",\"noData\":\"Transparency data is not available for this model.\",\"auditNote\":\"Audit Note\",\"learnMore\":\"Learn About Model Integrity\",\"tooltip\":\"Model Integrity measures how well documented and reproducible a model is across its lifecycle, serving as a quality gate for production deployment.\",\"architecturalProvenance\":\"Architectural Provenance\",\"architecturalProvenanceShort\":\"Architectural Provenance\",\"datasetComposition\":\"Dataset Composition\",\"datasetCompositionShort\":\"Dataset Composition\",\"tokenizerIntegrity\":\"Tokenizer Integrity\",\"tokenizerIntegrityShort\":\"Tokenizer Integrity\",\"parameterDensity\":\"Parameter Density\",\"parameterDensityShort\":\"Parameter Density\",\"trainingCompute\":\"Training Compute\",\"trainingComputeShort\":\"Training Compute\",\"benchmarkReproducibility\":\"Benchmark Reproducibility\",\"benchmarkReproducibilityShort\":\"Benchmark Reproducibility\",\"identityConsistency\":\"Identity Consistency\",\"identityConsistencyShort\":\"Identity Consistency\",\"licenseClarity\":\"License Clarity\",\"licenseClarityShort\":\"License Clarity\",\"hardwareFootprint\":\"Hardware Footprint\",\"hardwareFootprintShort\":\"Hardware Footprint\",\"versioningDrift\":\"Versioning Drift\",\"versioningDrift
16Short\":\"Versioning Drift\",\"viewFullReport\":\"View Full Report\",\"fullReportTitle\":\"{modelName} Model Integrity Report\",\"noReasoningProvided\":\"No detailed reasoning provided for this score.\",\"architecturalProvenanceTooltip\":\"Measures whether the model discloses its base architecture.\",\"datasetCompositionTooltip\":\"Evaluates disclosure of training data sources, language distribution, and dataset composition.\",\"tokenizerIntegrityTooltip\":\"Assesses transparency about the tokenizer used and whether it matches architectural claims.\",\"parameterDensityTooltip\":\"Measures clarity about total vs active parameters, especially important for MoE models.\",\"trainingComputeTooltip\":\"Evaluates disclosure of computational resources, GPU hours, and energy used in training.\",\"benchmarkReproducibilityTooltip\":\"Assesses whether claimed benchmark scores can be independently verified and replicated.\",\"identityConsistencyTooltip\":\"Checks whether the model correctly identifies itself without claiming to be a different model.\",\"licenseClarityTooltip\":\"Evaluates the clarity and legal soundness of the model's usage license and terms.\",\"hardwareFootprintTooltip\":\"Assesses accuracy of stated hardware requirements like VRAM for model deployment.\",\"versioningDriftTooltip\":\"Measures documentation of model versions, updates, and changes over time.\",\"disclaimerNote\":\"Note:\",\"disclaimerText\":\"This report is an automated AI assessment aggregating external data and manual internal audits. Final output is human-verified.\"}},\"LandingHero\":{\"deploy\":\"Deploy\",\"vramCalculator\":\"VRAM Calculator\",\"kerbToolkit\":\"Developer Toolkit\",\"compare\":\"Compare\",\"llmDirectory\":\"Model Directory\",\"benchmarks\":\"Benchmarks\",\"llmTrends\":\"LLM Trends\",\"learn\":\"Learn\",\"courses\":\"AI/ML Courses\",\"learningRoadmap\":\"Learning Roadmap\",\"engineeringBlog\":\"Engineering Blog\"},\"ModelsListPage\":{\"meta\":{\"titleDefault\":\"LLM Models List - Specs \u0026 GPU Requirements\",\"titleInferenceGpu\":\"LLMs That Run on {gpuLabel} - Compatibility \u0026 VRAM\",\"titleFinetuneGpu\":\"LLMs You Can Finetune with {method} on {gpuLabel}\",\"titleRunTypeGpu\":\"LLMs for {runType} on {gpuLabel}\",\"titleRank\":\"Best LLMs Ranked by Benchmark Performance\",\"titleCodingRank\":\"Best LLMs for Coding - Benchmark Rankings\",\"description\":\"Comprehensive list of LLM models. Compare parameter sizes, context lengths, benchmark scores, and GPU VRAM requirements.\"}},\"ModelTransparency\":{\"title\":\"Model Transparency\",\"lastUpdated\":\"Last Updated: February 19, 2026\",\"intro\":{\"highlight\":\"Evaluating AI models through evidence-based transparency scoring\",\"paragraph1\":\"The AI industry is plagued by unverifiable claims, benchmark gaming, and opaque model development practices. ApX Model Transparency addresses this by systematically evaluating how transparent AI model providers are about their models' architecture, training, and deployment, empowering developers and researchers to make informed decisions.\",\"paragraph2\":\"This transparency scoring system evaluates models across 10 distinct criteria organized into three pillars: Upstream (where the model comes from), Model (the model itself), and Downstream (how you use it). Each criterion is scored from 0-10 based on publicly verifiable evidence, with a total maximum score of 100 points.\",\"paragraph3\":\"This system is inspired by \",\"paragraph3Link\":\"Stanford's Foundation Model Transparency Index (FMTI)\",\"paragraph3End\":\" but simplified and adapted for practical application by developers and practitioners, rather than purely academic assessment.\"},\"whyMatters\":{\"title\":\"Why Model Transparency Matters\",\"paragraph1\":\"The lack of transparency in AI development has created significant challenges for the technical community:\",\"issue1Title\":\"Benchmark Score Gaming:\",\"issue1Text\":\"Models claim higher performance through undisclosed prompt engineering, contaminated test data, or cherry-picked evaluation sets without reproducible methodology.\",\"
16issue2Title\":\"Architecture Obfuscation:\",\"issue2Text\":\"Providers make vague claims like \\\"advanced transformer architecture\\\" without disclosing whether the model is an original creation, fine-tuned from another base model, or distilled from a proprietary system.\",\"issue3Title\":\"Parameter Misrepresentation:\",\"issue3Text\":\"Mixture-of-Experts (MoE) models advertise massive parameter counts (e.g., \\\"47B parameters\\\") while only activating a fraction (e.g., 12B active parameters) during inference, misleading users about computational requirements.\",\"issue4Title\":\"Silent Model Degradation:\",\"issue4Text\":\"Model weights or behavior change without version updates or announcements, causing unexpected performance shifts, increased refusals, or \\\"alignment tax\\\" where safety tuning degrades capability.\",\"issue5Title\":\"Licensing Confusion:\",\"issue5Text\":\"Models are labeled \\\"open source\\\" while imposing significant commercial restrictions, or license terms conflict with data provenance, creating legal uncertainty for developers.\",\"paragraph2\":\"By scoring transparency across technical, legal, and operational dimensions, this provides a systematic framework to hold model providers accountable and help users select models that meet their integrity standards.\"},\"framework\":{\"title\":\"The Three-Pillar Framework\",\"intro\":\"The transparency evaluation is organized into three pillars that cover the complete lifecycle of an AI model:\"},\"pillars\":{\"upstream\":{\"badge\":\"30 Points\",\"title\":\"Upstream: Model Origins\",\"description\":\"Evaluates transparency about where the model came from: its base architecture, training data sources, and tokenization approach. Essential for understanding the foundation upon which the model is built.\"},\"model\":{\"badge\":\"40 Points\",\"title\":\"Model: Core Characteristics\",\"description\":\"Assesses transparency about the model itself: parameter counts, training compute, benchmark validity, and identity consistency. The largest pillar because these factors directly impact model selection and trust.\"},\"downstream\":{\"badge\":\"30 Points\",\"title\":\"Downstream: Practical Usage\",\"description\":\"Examines transparency about how the model can be used: licensing terms, hardware requirements, and version management. Critical for deployment planning and long-term maintenance.\"}},\"criteria\":{\"title\":\"The 10 Transparency Criteria\",\"intro\":\"Each model is evaluated against 10 specific criteria. Scores are based on publicly verifiable evidence. Vague marketing claims or unverifiable statements result in low scores.\",\"scoring\":{\"highScore\":\"High Score (7-10):\",\"mediumScore\":\"Medium Score (5-6):\",\"lowScore\":\"Low Score (0-4):\"},\"upstream\":{\"sectionTitle\":\"Pillar 1: Upstream Transparency\",\"architectural\":{\"title\":\"1. Architectural Provenance (0-10)\",\"description\":\"Does the provider disclose the base model architecture, whether it was trained from scratch or fine-tuned, and what modifications were made?\",\"highScore\":\"Base model explicitly named with public documentation, training methodology fully described, architectural modifications documented, pretraining procedure detailed with evidence.\",\"mediumScore\":\"Base model mentioned but documentation limited, training approach described in general terms, some architectural details provided but not comprehensive, partial pretraining information.\",\"lowScore\":\"No base model disclosure, vague claims like \\\"built on transformer architecture\\\", undisclosed fine-tuning approach, \\\"proprietary training methods\\\" without documentation.\"},\"dataset\":{\"title\":\"2. Dataset Composition (0-10)\",\"description\":\"Are the training data sources disclosed? Is the dataset composition breakdown available (e.g., web 40%, code 20%, books 10%)?\",\"highScore\":\"Training data sources disclosed publicly, dataset composition breakdown provided, filtering and cleaning methodology documented, data collection approach explained, sample data available.\",\"mediumScore\":\"Some major data sources mentioned, partial composition information provided, basic filtering approach described, limited documentation on collection methods.\",\"lowScore\":\"Vague claims like \\\"trained on diverse internet data\\\", no sources named, \\\"carefully curated\\\" without defining criteria, \\\"proprietary dataset\\\" with no details.\"},\"tokenizer\":{\"title\":\"3. Tokenizer Integrity (0-10)\",\"description\":\"Is the tokenizer publicly available for inspection? Does it match the claimed language support and training data?\",\"highScore\":\"Tokenizer publicly available, vocabulary size stated, tokenization approach documented, verifiable alignment with language support, training data composition matches tokenizer design.\",\"mediumScore\":\"Tokenizer available but documentation sparse, vocabulary size provided, basic tokenization details given, language support claims generally align with observable behavior.\",\"lowScore\":\"No tokenizer access, unknown vocabulary size, vague \\\"advanced tokenization\\\", claims multilingual support but tokenizer not inspectable, token counts don't match across platforms.\"}},\"model\":{\"sectionTitle\":\"Pillar 2: Model Transparency\",\"parameter\":{\"title\":\"4. Parameter Density (0-10)\",\"description\":\"Are total and active parameters clearly stated? For MoE models, is the distinction between total and active parameters transparent?\",\"highScore\":\"Total parameters clearly stated, active parameters disclosed for MoE models, architectural breakdown provided (e.g., attention 40%, FFN 60%), quantization impact documented with evidence.\",\"mediumScore\":\"Parameter counts provided but some ambiguity, MoE models mention active parameters but lack detail, basic architectural information given, limited quantization documentation.\",\"lowScore\":\"Vague parameter counts (\\\"~7B\\\", \\\"approximately\\\"), MoE models advertising total params without active params disclosure, conflicting parameter counts across sources, no dense vs sparse clarification.\"},\"compute\":{\"title\":\"5. Training Compute (0-10)\",\"description\":\"Is information about training compute resources disclosed? GPU/TPU hours, hardware specifications, environmental impact?\",\"highScore\":\"GPU/TPU hours disclosed, hardware specifications provided, training duration stated, carbon footprint calculated or estimated, cost transparency where appropriate.\",\"mediumScore\":\"General compute information provided, hardware type mentioned, approximate training duration, limited environmental impact data, some cost indicators.\",\"lowScore\":\"Vague claims like \\\"trained on powerful GPUs\\\", no compute hours disclosed, no environmental impact data, \\\"significant resources\\\" without specifics, downplaying resource requirements.\"},\"benchmark\":{\"title\":\"6. Benchmark Reproducibility (0-10)\",\"description\":\"Can benchmark results be reproduced? Are evaluation prompts, few-shot examples, and benchmark versions disclosed?\",\"highScore\":\"Evaluation code public, exact prompts and few-shot examples disclosed, benchmark versions specified, reproduction instructions provided, third-party verification available or encouraged.\",\"mediumScore\":\"Some evaluation details provided, benchmark versions mentioned, general methodology described, partial reproduction possible, limited third-party verification.\",\"lowScore\":\"Cherry-picked benchmarks, no evaluation methodology, vague \\\"outperforms competitors\\\", undisclosed prompting strategies, no reproduction path, different scores across sources without explanation.\"},\"identity\":{\"title\":\"7. Identity Consistency (0-10)\",\"description\":\"Does the model correctly identify itself? Is version information provided? Does it accurately represent its capabilities?\",\"highScore\":\"Model correctly identifies itself consistently, version number provided and accurate, no identity confusion, transparent about capabilities and limitations, acknowledges knowledge cutoff dates.\",\"mediumScore\":\"Model generally identifies itself correctly, version info sometimes provided, mostly accurate capability claims, occasional minor inconsistencies, limited limitation disclosure.\",\"lowScore\":\"Claims to be different model (e.g., says it's GPT-4 when it's not), identity confusion, misleading capability claims, no version awareness, pretends to be from different c
16ompany.\"}},\"downstream\":{\"sectionTitle\":\"Pillar 3: Downstream Transparency\",\"license\":{\"title\":\"8. License Clarity (0-10)\",\"description\":\"Is the license clear and unambiguous? Are commercial use terms explicit? Are there conflicting license claims?\",\"highScore\":\"Clear open source license (Apache 2.0, MIT) or well-defined custom license, commercial use terms explicit, no conflicting terms, derivative works policy clear, consistent licensing across weights and code.\",\"mediumScore\":\"License specified but some terms unclear, commercial use generally permitted with some restrictions, mostly consistent licensing, derivative works policy mentioned but not detailed.\",\"lowScore\":\"Vague licensing, conflicting terms, \\\"free for non-commercial use\\\" without clear definition, license unclear or missing, \\\"open source\\\" label with commercial restrictions (not true open source).\"},\"hardware\":{\"title\":\"9. Hardware Footprint (0-10)\",\"description\":\"Are VRAM requirements documented for different precision levels? Is guidance provided for quantization and context length scaling?\",\"highScore\":\"VRAM requirements documented for FP16/Q8/Q4, batch size impact disclosed, context length memory scaling provided, quantization accuracy tradeoffs documented, realistic requirements stated.\",\"mediumScore\":\"Basic VRAM requirements provided, some precision levels covered, general quantization guidance given, context length considerations mentioned, requirements mostly realistic.\",\"lowScore\":\"No VRAM guidance, vague \\\"runs on consumer hardware\\\" that doesn't match reality, misleading efficiency claims, claims \\\"8GB VRAM sufficient\\\" when 24GB actually needed, undisclosed context limitations.\"},\"versioning\":{\"title\":\"10. Versioning \u0026 Drift (0-10)\",\"description\":\"Is semantic versioning used? Are changes documented? Can users access previous versions if needed?\",\"highScore\":\"Semantic versioning used, changelog maintained, API and weight changes documented, deprecation notices provided, version history accessible, clear migration paths for breaking changes.\",\"mediumScore\":\"Basic versioning in place, some changes documented, major updates announced, limited version history available, general migration guidance provided.\",\"lowScore\":\"No versioning system, silent updates, behavior drift without notice, no changelog, impossible to track changes, model weights change without version updates, no access to previous versions.\"}}},\"scoring\":{\"title\":\"Scoring Methodology\",\"intro\":\"Each criterion is scored on a 0-10 scale based on the quality and accessibility of publicly available evidence:\",\"scale\":{\"excellent\":\"Exemplary transparency, comprehensive documentation\",\"good\":\"Good transparency with minor gaps\",\"moderate\":\"Moderate transparency, key details missing\",\"minimal\":\"Minimal disclosure with significant gaps\",\"none\":\"No information, vague claims, or unverifiable assertions\"},\"grades\":{\"title\":\"Overall Transparency Grades\",\"a\":\"Exceptional transparency, exemplary practices across all pillars.\",\"b\":\"Good transparency with minor gaps, mostly trustworthy.\",\"c\":\"Moderate transparency, significant gaps but usable information.\",\"d\":\"Poor transparency, major concerns about verifiability.\",\"f\":\"Opaque, untrustworthy, or actively deceptive practices.\"}},\"penalties\":{\"title\":\"Automatic Penalties for Known Issues\",\"intro\":\"The scoring system applies automatic penalties when specific controversies or violations are discovered:\",\"contamination\":{\"title\":\"Benchmark Contamination (-2 to -5 points):\",\"description\":\"Training data includes test sets, contamination not disclosed publicly, or evaluation methodology designed to inflate scores artificially.\"},\"misrepresentation\":{\"title\":\"Identity Misrepresentation (-3 to -8 points):\",\"description\":\"Model falsely claims to be a competitor's model, inflates parameter counts, or makes misleading capability claims that can't be verified.\"},\"licensing\":{\"title\":\"License Violations (-5 to -10 points):\",\"description\":\"Using restricted data without permission, violating upstream model licenses, or significant conflicts between stated license and actual terms of service.\"},\"degradation\":{\"title\":\"Silent Model Degradation (-3 to -6 points):\",\"description\":\"Performance degraded without notice, safety restrictions increased silently (alignment tax), or behavior changes without version updates.\"},\"provenance\":{\"title\":\"Data Provenance Issues (-4 to -7 points):\",\"description\":\"Copyrighted material used without disclosure, personal data harvested without consent, or undisclosed use of synthetic data from other proprietary models.\"},\"fairness\":\"Note: Models are not penalized for technical oversights, unintentional bugs, or legally mandated content restrictions (e.g., regional compliance requirements). Penalties focu
16s on deliberate obfuscation or deceptive practices.\"},\"methodology\":{\"title\":\"Research \u0026 Verification Methodology\",\"intro\":\"Transparency evaluations combine AI-powered research with human verification. Multiple sources of evidence are used and claims are cross-referenced to ensure accuracy:\",\"sources\":{\"title\":\"Evidence Hierarchy (strongest to weakest):\",\"academic\":\"Peer-reviewed papers with reproducible results\",\"repos\":\"Official GitHub repositories with actual model code and documentation\",\"blogs\":\"Official technical blog posts with detailed specifications\",\"audits\":\"Independent third-party technical audits and testing\",\"docs\":\"Official model cards, datasheets, and documentation\",\"api\":\"API documentation with specifications\"},\"verification\":{\"title\":\"Verification Activities by Type\",\"description\":\"Where possible, external sources are supplemented with hands-on verification using different techniques depending on the criterion:\",\"documentary\":{\"title\":\"Documentary Review:\",\"description\":\"License analysis, policy review, documentation completeness assessment, changelog examination. Used for: License Clarity, Versioning \u0026 Drift, Dataset Composition (partial).\"},\"staticAnalysis\":{\"title\":\"Static Analysis:\",\"description\":\"Downloading and inspecting tokenizer files, model weights, configuration files, code repositories. Vocabulary size verification, architecture inspection, parameter counting. Used for: Tokenizer Integrity, Architectural Provenance, Parameter Density.\"},\"interactiveTesting\":{\"title\":\"Interactive Testing:\",\"description\":\"Querying deployed models to detect tokenizer behavior, testing identity consistency (self-identification), validating capability claims. Used for: Identity Consistency, Tokenizer Integrity, Benchmark Reproducibility (partial).\"},\"handsonDeployment\":{\"title\":\"Hands-On Deployment:\",\"description\":\"Actually running models locally or in test environments to measure VRAM consumption, validate context length limits, verify quantization claims, test inference speed. Used for: Hardware Footprint, Parameter Density (validation), Compute efficiency claims.\"},\"crossReference\":{\"title\":\"Cross-Reference Validation:\",\"description\":\"Comparing claims across multiple sources, checking for consistency between documentation and observed behavior, validating third-party reports. Applied across all criteria to detect inconsistencies.\"}}},\"focus\":{\"title\":\"Technical Transparency vs. Safety Transparency\",\"paragraph1\":\"Model Transparency focuses on technical transparency: the information developers and practitioners need to effectively evaluate, deploy, and maintain AI models. This includes architecture details, resource requirements, licensing clarity, and operational characteristics.\",\"paragraph2\":\"While several excellent initiatives focus on safety transparency (bias auditing, red-teaming results, content moderation approaches), the emphasis is deliberately on the technical infrastructure layer. Technical transparency is considered a prerequisite for informed model selection and effective deployment.\",\"technical\":{\"title\":\"Focus: Technical Transparency\",\"description\":\"Architecture, training data, compute resources, benchmarks, licensing, versioning, hardware requirements: information for building with models.\"},\"safety\":{\"title\":\"Complementary: Safety Transparency\",\"description\":\"Bias testing, harmful content evaluation, safety benchmarks, red-teaming results: covered by other initiatives like Stanford HELM and AI Verify.\"}},\"viewScores\":{\"title\":\"View Model Transparency Scores\",\"paragraph1\":\"Transparency scores are displayed on individual model pages in the LLM database. Each model includes a transparency chart showing scores across all 10 criteria, along with an overall transparency grade.\",\"paragraph2\":\"Not all models have transparency scores yet. Coverage is actively being expanded. Models are evaluated based on publicly available information at the time of assessment and may be re-evaluated as new information becomes available.\",\"cta\":{\"title\":\"Explore Transparency Scores\",\"description\":\"Browse the LLM database to view transparency evaluations for individual models.\",\"button\":\"View LLM Database â\"}}},\"RoadmapPage\":{\"meta\":{\"title\":\"My Learning Roadmap - ApX Machine Learning\",\"description\":\"Plan and organize your AI/ML learning path with a customizable course roadmap.\"},\"title\":\"My Learning Roadmap\",\"subtitle\":\"Build your personalized AI/ML path. Drag and drop courses to create your ideal learning sequence.\",\"emptyState\":{\"title\":\"Start Building Your Learning Path\",\"description\":\"Add courses to create a personalized roadmap. Use Smart Sort to automatically organize them in the optimal learning sequence.\",\"browseCourses\":\"Add Courses\",\"getIdeas\":\"Get Roadmap Ideas\",\"smartSortDisabled\":\"Smart Sort (Add 2+ courses to enable)\"},\"addCourse\":\"Add Course\",\"noCourses\":\"No courses available\",\"courseCard\":{\"level\":\"Level\",\"duration\":\"~{hours} hours\",\"goToCourse\":\"View Course\",\"continueCourse\":\"Continue\",\"removeCourse\":\"Remove from Roadmap\",\"whatsNext\":\"What's Next?\"},\"recommendations\":{\"title\":\"Course Recommendations\",\"whatsNext\":\"What's Next\",\"prerequisites\":\"Prerequisites\",\"about\":\"About\",\"nextDescription\":\"Here are some suggested courses you can look into next.\",\"prerequisitesDescription\":\"These courses may help prepare you for this course.\",\"highlightedRecommended\":\"are highly recommended.\",\"noNextCourses\":\"No suggested next courses available.\",\"noPrerequisites\":\"No prerequisite courses found.\",\"noPrerequisitesBeginner\":\"This is an introductory course and can be taken with no prior knowledge.\",\"aboutTitle\":\"Course Recommendations\",\"aboutDescription\":\"Get personalized course suggestions to help you plan your learning journey. We recommend courses based on prerequisites and natural learning progression.\",\"howItWorks\":\"How it works\",\"howItWorksNext\"
16:\"What's Next: Shows courses that build upon what you've learned\",\"howItWorksPrerequisites\":\"Prerequisites: Shows courses that prepare you for this course\",\"howItWorksStarred\":\"Starred courses are especially recommended\",\"tip\":\"Tip\",\"tipDescription\":\"Add recommended courses to your roadmap to create a structured learning path.\"},\"smartSort\":{\"button\":\"Smart Sort\",\"tooltip\":\"Smart sorting that analyzes course relationships and learning outcomes to automatically arrange your roadmap in the most effective learning sequence.\",\"undo\":\"Undo Sort\"}},\"CourseRoadmap\":{\"meta\":{\"baseTitle\":\"AI Machine Learning Roadmap \u0026 Courses\",\"description\":\"Follow a clear, structured path designed to take you from introductory concepts to achieving your ML goals\",\"goals\":{\"Foundation\":\"Foundation\",\"CareerChange\":\"Career Change\",\"Upskilling\":\"Upskilling\",\"Specialization\":\"Specialization\",\"NovelSkills\":\"Novel Skills\",\"Certification\":\"Certification\",\"FutureProofing\":\"Future Proofing\",\"comprehensive\":\"Complete Curriculum\"}},\"pageTitle\":\"Learning Paths\",\"pageSubtitle\":\"Structured learning paths to help reach specific goals\",\"comprehensivePageTitle\":\"Complete AI/ML Curriculum\",\"comprehensivePageSubtitle\":\"Comprehensive stage-based progression from foundations to cutting-edge AI\",\"sidebarTitle\":\"Learning Paths\",\"careerTab\":\"Breaking into AI\",\"careerTabDesc\":\"Foundation to job-ready\",\"careerTabInfo\":\"Complete curricula designed to take learners from fundamentals to job-ready skills in their target role.\",\"specializationTab\":\"Upskill \u0026 Specialize\",\"specializationTabDesc\":\"Deepen expertise or strengthen foundations\",\"specializationTabInfo\":\"Focused tracks to deepen domain expertise, strengthen mathematical foundations, or explore new areas of AI.\",\"comprehensiveTab\":\"Complete Curriculum\",\"comprehensiveTabDesc\":\"All courses by stage \u0026 difficulty\",\"comprehensiveTabInfo\":\"Browse the complete course catalog organized by learning stages, from foundational concepts to specialized frontiers.\",\"browseAllCta\":{\"title\":\"Not sure which track fits?\",\"description\":\"Browse all courses to find what matches specific interests.\",\"button\":\"Browse All Courses\"},\"registerCta\":{\"title\":\"Track your progress\",\"description\":\"Create a free account to save your roadmap, track course progress, and earn a verified certificate.\",\"button\":\"Create Free Account\"},\"goalNames\":{\"Foundation\":\"Foundation\",\"CareerChange\":\"Career Change\",\"Upskilling\":\"Upskilling\",\"Specialization\":\"Specialization\",\"NovelSkills\":\"Novel Skills\",\"Certification\":\"Certification\",\"FutureProofing\":\"Future Proofing\"},\"goalDescriptions\":{\"Foundation\":\"Building fundamental and core knowledge\",\"CareerChange\":\"Transitioning to an AI/ML role from another field\",\"Upskilling\":\"Enhancing skills in AI/ML for career growth\",\"Specialization\":\"Develop deep expertise in a specific area\",\"NovelSkills\":\"Cutting-edge or emerging technologies\",\"Certification\":\"Preparing for certifications or formal qualifications\",\"FutureProofing\":\"Staying ahead of industry trends and advancements\"},\"sorting\":{\"howSorted\":\"How are these courses sorted?\",\"title\":\"Smart Graph-Based Sorting\",\"description\":\"Courses are intelligently ordered using a dependency graph algorithm that analyzes course relationships through AI embeddings.\",\"howItWorks\":\"How it works:\",\"step1\":\"The system creates semantic embeddings for each course's prerequisites, learning outcomes, and recommended next courses\",\"step2\":\"The algorithm builds a directed graph where courses are nodes and learning paths are weighted edges\",\"step3\":\"It calculates two key scores:\",\"prerequisiteScore\":\"Prerequisite score: How much Course A's outcomes match Course B's prerequisites\",\"nextCourseScore\":\"Next course score: How well Course B aligns with what comes after Course A\",\"step4\":\"A topological sort with weighted priorities determines the optimal order, favoring:\",\"fewerDeps\":\"Courses with fewer dependencies first\",\"fundamentals\":\"Fundamentals courses before specialized topics\",\"naturalProgression\":\"Natural skill progression (introductory â specialist)\",\"conclusion\":\"This approach ensures courses appear in an order that makes sense for learning, with foundational topics before advanced ones, and related courses grouped logically.\"},\"comprehensive\":{\"coursesAvailable\":\"{count} courses available across all learning tracks\",\"pageOf\":\"Page {current} of {total}\",\"noCourses\":\"No courses available yet.\",\"hours\":\"{hours} hours\",\"viewCourse\":\"View Course\"},\"search\":{\"title\":\"AI Learning Roadmap\",\"badge\":\"Vector Search\",\"description\":\"Use natural language to find the perfect learning track. 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16\",\"noResults\":\"No tracks found. Try adjusting your search.\",\"noResultsDescription\":\"Try different keywords or browse all available tracks below\",\"searching\":\"Searching tracks...\",\"learnMore\":\"How it works\"},\"trackDetails\":{\"tracksAvailable\":\"{count} {count, plural, =1 {track} other {tracks}} available\",\"selectTrack\":\"Select a Track\",\"courses\":\"{count} {count, plural, =1 {Course} other {Courses}}\",\"addToRoadmap\":\"Add to Roadmap\",\"addToRoadmapTooltip\":\"Add all {count} {count, plural, =1 {course} other {courses}} to your personal learning roadmap\",\"loginToAddToRoadmap\":\"Login to add to roadmap\",\"noCourses\":\"No courses available in this track yet.\",\"selectTrackPrompt\":\"Select a track to see courses\",\"showLess\":\"Show Less\",\"showMoreTracks\":\"Show {count} More Tracks\",\"view\":\"View\",\"trackAdded\":\"Track Added\",\"trackAddedMessage\":\"Added {added} {added, plural, =1 {course} other {courses}} to your roadmap\",\"trackAddedPartial\":\"Added {added} {added, plural, =1 {course} other {courses}} to your roadmap. {skipped} {skipped, plural, =1 {course was} other {courses were}} already in your roadmap.\",\"alreadyInRoadmap\":\"Already in Roadmap\",\"alreadyInRoadmapMessage\":\"These courses are all already in your roadmap\",\"error\":\"Error\",\"errorMessage\":\"Failed to add track to roadmap. Please try again.\"},\"sidebar\":{\"breakIntoAi\":{\"title\":\"Break Into AI\",\"description\":\"Get started with no prior experience\"},\"upskillSpecialize\":{\"title\":\"Upskill \u0026 Specialize\",\"description\":\"Advance your existing foundational skills\"},\"or\":\"OR\",\"completeCurriculum\":{\"title\":\"Complete Curriculum\",\"description\":\"Full roadmap to world-class AI engineer\"},\"careerInfo\":\"Structured paths designed for career changers and those newer to the AI field.\",\"specializationInfo\":\"Focused learning tracks for students and professionals looking to deepen expertise in specific AI domains.\",\"comprehensiveInfo\":\"Complete stage-by-stage curriculum from foundations to cutting-edge research. View all courses organized by difficulty and topic.\"},\"faq\":{\"title\":\"FAQ\",\"questions\":{\"required\":{\"q\":\"Is every course required?\",\"a\":\"Core modules are essential, but some courses can be substituted based on background.\"},\"mathLevel\":{\"q\":\"What math level is needed?\",\"a\":\"Career tracks: basic college math helps. Specialization: requires linear algebra, calculus, probability.\"},\"duration\":{\"q\":\"How long to complete?\",\"a\":\"Estimates assume 5-10 hours/week. Actual time varies by pace and prior knowledge.\"},\"codingExperience\":{\"q\":\"Coding experience required?\",\"a\":\"Career tracks start from basics. Specialization tracks expect Python comfort.\"},\"switchTracks\":{\"q\":\"Can I switch tracks?\",\"a\":\"Absolutely. Many courses appear in multiple tracks and skills transfer across domains.\"}}},\"pathCard\":{\"viewModules\":\"View Modules\",\"hideModules\":\"Hide Modules\",\"whatYoullLearn\":\"What You'll Learn\",\"curriculum\":\"Curriculum\",\"module\":\"Module {number}: {title}\",\"courses\":\"{count} courses\"},\"categories\":{\"core\":\"Core\",\"electives\":\"Electives\",\"specialization\":\"Specialization\"},\"levels\":{\"beginner\":\"Introductory\",\"intermediate\":\"Professional\",\"advanced\":\"Specialist\",\"expert\":\"Expert\"},\"careerPaths\":{\"llmDeveloper\":{\"title\":\"LLM Application Developer\",\"description\":\"Build production-ready generative AI applications from scratch. Implement, deploy, and optimize LLM-powered systems.\",\"duration\":\"4-6 months\",\"outcomes\":{\"0\":\"Build and deploy LLM applications with Python\",\"1\":\"Implement RAG systems for enhanced context\",\"2\":\"Optimize models for production environments\",\"3\":\"Design agentic workflows and multi-step reasoning\"},\"modules\":{\"foundations\":\"Foundations\",\"applicationTooling\":\"Application \u0026 Tooling\",\"deployment\":\"Deployment\",\"optimization\":\"Optimization\"}},\"mlResearcher\":{\"title\":\"Deep Learning Researcher\",\"description\":\"Understand how models work from the ground up. Build neural networks and transformers from scratch using modern frameworks.\",\"duration\":\"6-9 months\",\"outcomes\":{\"0\":\"Master the mathematics behind neural networks\",\"1\":\"Implement transformers and attention mechanisms\",\"2\":\"Design custom architectures for specific tasks\",\"3\":\"Contribute to open-source ML research projects\"},\"modules\":{\"mathFoundations\":\"Mathematical Foundations\",\"coreDeepLearning\":\"Core Deep Learning\",\"advancedArchitectures\":\"Advanced Architectures\",\"capstone\":\"Capstone\"}},\"mlopsEngineer\":{\"title\":\"MLOps \u0026 Deployment Specialist\",\"description\":\"Focus on the engineering and infrastru
16cture needed to take models from notebooks to production at scale.\",\"duration\":\"5-7 months\",\"outcomes\":{\"0\":\"Build CI/CD pipelines for ML workflows\",\"1\":\"Monitor and maintain models in production\",\"2\":\"Implement experiment tracking and versioning\",\"3\":\"Deploy and scale LLM inference systems\"},\"modules\":{\"engineeringFoundations\":\"Engineering Foundations\",\"coreMLOps\":\"Core MLOps\",\"productionSystems\":\"Production Systems\",\"specialization\":\"Specialization\"}}},\"specializationPaths\":{\"mathFoundations\":{\"title\":\"Mathematical Foundations\",\"description\":\"Strengthen mathematical foundation. Master linear algebra, calculus, and statistics needed for advanced ML work.\",\"duration\":\"3-4 months\",\"outcomes\":{\"0\":\"Apply linear algebra to understand neural network operations\",\"1\":\"Use calculus for optimization and backpropagation\",\"2\":\"Apply probability and statistics for model evaluation\",\"3\":\"Read and implement research papers confidently\"},\"modules\":{\"coreMathematics\":\"Core Mathematics\"}},\"llmFineTuning\":{\"title\":\"LLM Fine-Tuning \u0026 Customization\",\"description\":\"Adapt and fine-tune models for domain-specific tasks using efficient techniques like LoRA and PEFT.\",\"duration\":\"3-4 months\",\"outcomes\":{\"0\":\"Fine-tune models with custom datasets\",\"1\":\"Apply LoRA and PEFT for efficient training\",\"2\":\"Implement RLHF for alignment\",\"3\":\"Deploy custom models in production\"},\"modules\":{\"fineTuningFundamentals\":\"Fine-Tuning Fundamentals\",\"efficientTraining\":\"Efficient Training\",\"productionDeployment\":\"Production Deployment\"}},\"multiAgent\":{\"title\":\"Multi-Agent LLM Systems\",\"description\":\"Design sophisticated agentic systems and build autonomous agents that collaborate, plan, and execute complex tasks.\",\"duration\":\"2-3 months\",\"outcomes\":{\"0\":\"Design multi-agent collaboration patterns\",\"1\":\"Build agents with memory and state\",\"2\":\"Create custom tools and integrations\",\"3\":\"Implement autonomous task execution\"},\"modules\":{\"agentFoundations\":\"Agent Foundations\",\"advancedAgentSystems\":\"Advanced Agent Systems\",\"memoryState\":\"Memory \u0026 State\"}},\"computerVision\":{\"title\":\"Computer Vision\",\"description\":\"Apply deep learning to visual data. Master CNNs, vision transformers, and modern architectures for image and video tasks.\",\"duration\":\"4-5 months\",\"outcomes\":{\"0\":\"Build CNN-based classification systems\",\"1\":\"Implement diffusion models for image generation\",\"2\":\"Train GANs for synthetic data\",\"3\":\"Deploy vision models at scale\"},\"modules\":{\"foundations\":\"Foundations\",\"cnnArchitectures\":\"CNN Architectures\",\"generativeModels\":\"Generative Models\",\"deployment\":\"Deployment\"}},\"nlp\":{\"title\":\"Natural Language Processing\",\"description\":\"Master NLP techniques from fundamentals to advanced applications. Work with text data, embeddings, and language understanding.\",\"duration\":\"3-4 months\",\"outcomes\":{\"0\":\"Process and analyze text data effectively\",\"1\":\"Build semantic search systems\",\"2\":\"Implement embeddings and vector databases\",\"3\":\"Create NLP pipelines for production\"},\"modules\":{\"nlpFundamentals\":\"NLP Fundamentals\",\"advancedTechniques\":\"Advanced Techniques\"}},\"dataScience\":{\"title\":\"Data Science \u0026 Analytics\",\"description\":\"Build expertise in data analysis, visualization, and statistical modeling. Master tools and techniques for extracting insights.\",\"duration\":\"3-4 months\",\"outcomes\":{\"0\":\"Analyze and visualize complex datasets\",\"1\":\"Clean and preprocess data for modeling\",\"2\":\"Build statistical models and forecasts\",\"3\":\"Extract actionable insights from data\"},\"modules\":{\"dataFoundations\":\"Data Foundations\",\"advancedAnalytics\":\"Advanced Analytics\"}},\"reinforcementLearning\":{\"title\":\"Reinforcement Learning\",\"description\":\"Train agents that learn through interaction. Master RL algorithms from basics to advanced techniques.\",\"duration\":\"4-5 months\",\"outcomes\":{\"0\":\"Implement RL algorithms from scratch\",\"1\":\"Train agents for complex environments\",\"2\":\"Apply RLHF for LLM alignment\",\"3\":\"Design reward functions effectively\"},\"modules\":{\"rlFoundations\":\"RL Foundations\",\"advancedRL\":\"Advanced RL\"}},\"dataEngineering\":{\"title\":\"Data Engineering \u0026 Pipelines\",\"description\":\"Build scalable data infrastru
16cture, design ETL pipelines, manage databases, and handle large-scale data processing.\",\"duration\":\"3-4 months\",\"outcomes\":{\"0\":\"Design and build ETL pipelines\",\"1\":\"Manage databases and data storage\",\"2\":\"Handle large-scale data processing\",\"3\":\"Implement data versioning systems\"},\"modules\":{\"foundations\":\"Foundations\",\"etlPipelines\":\"ETL \u0026 Pipelines\"}}},\"stages\":{\"stage1\":{\"title\":\"Stage 1: Foundational AI\",\"description\":\"Build core knowledge in mathematics, programming, data science, and the basics of AI and LLMs.\",\"tracks\":{\"mathCS\":\"Mathematics \u0026 CS Foundations\",\"coreML\":\"Core ML \u0026 Data Science\",\"introAI\":\"Introduction to AI \u0026 LLMs\"}},\"stage2\":{\"title\":\"Stage 2: Applied AI\",\"description\":\"Get into deep learning, develop LLM applications, and learn the basics of ML engineering.\",\"tracks\":{\"deepLearning\":\"Deep Learning Specialization\",\"llmGenerative\":\"LLM \u0026 Generative AI Development\",\"mlEngineering\":\"ML Engineering \u0026 MLOps Basics\"}},\"stage3\":{\"title\":\"Stage 3: Advanced AI Systems\",\"description\":\"Master advanced LLM techniques, explore specialized AI applications, and scale systems with production MLOps.\",\"tracks\":{\"advancedLLM\":\"Advanced LLM Techniques\",\"specializedAI\":\"Specialized AI Applications\",\"productionMLOps\":\"Production MLOps \u0026 Scalability\"}},\"stage4\":{\"title\":\"Stage 4: Specialized AI Frontiers\",\"description\":\"Explore cutting-edge research in LLMs, advanced AI systems, and theoretical concepts.\",\"tracks\":{\"cuttingEdgeLLM\":\"Cutting-Edge LLM Research\",\"advancedSystems\":\"Advanced AI Systems \u0026 Theory\"}}},\"CourseList\":{\"levels\":{\"beginner\":\"Introductory\",\"intermediate\":\"Professional\",\"advanced\":\"Specialist\",\"expert\":\"Expert\",\"unknown\":\"Unknown\"}}},\"PrivacyPage\":{\"title\":\"Privacy Policy\",\"lastUpdated\":\"Last Updated: June 17, 2026\",\"intro1\":\"ApX Machine Learning, a service provided by APX MACHINE LEARNING TECHNOLOGIES (Registration No: 202603008008 (003810963-D)), a company incorporated in Malaysia (\\\"we\\\", \\\"our\\\", \\\"us\\\"), is committed to protecting and respecting your privacy. This Privacy Policy explains how we collect, use, disclose, and safeguard your information when you visit our website and use our services (\\\"the Site\\\").\",\"intro2\":\"Please read this privacy policy carefully. If you do not agree with the terms of this privacy policy, please do not access the site. By using the Site, you consent to the data practices described in this policy.\",\"section1\":{\"title\":\"1. Information We Collect\",\"text1\":\"We may collect information about you in a variety of ways. The information we may collect on the Site includes:\",\"personal\":{\"title\":\"Personal Information:\",\"item1\":\"Name, email address, and password when you create an account\",\"item2\":\"Profile information you choose to provide\",\"item3\":\"Communication preferences and marketing opt-ins\"},\"payment\":{\"title\":\"Payment Information:\",\"item1\":\"Billing address and payment method details (processed securely through third-party payment processors)\",\"item2\":\"Transaction history and purchase records\",\"item3\":\"We do not store full credit card numbers on our servers\"},\"course\":{\"title\":\"Course and Learning Data:\",\"item1\":\"Course enrollment and progress information\",\"item2\":\"Quiz and assessment results\",\"item3\":\"Certificate completion records\",\"item4\":\"Time spent on courses and learning activity\",\"item5\":\"User-generated content (comments, reviews)\"},\"technical\":{\"title\":\"Technical Data:\",\"item1\":\"IP address, browser type and version, device information\",\"item2\":\"Time zone settings, operating system\",\"item3\":\"Access times, pages visited, and referring URLs\",\"item4\":\"Cookies and similar tracking technologies\"},\"chatbot\":{\"title\":\"AI Chatbot Data:\",\"item1\":\"Conversation messages and queries you submit to the AI assistant\",\"item2\":\"Chat session context retained during your active session\"}},\"section2\":{\"title\":\"2. Children's Privacy\",\"text1\":\"Our services are intended for users aged 13 and older. We do not knowingly collect personal information from children under 13 years of age. If you are under 13, please do not use the Site or provide any personal information.\",\"text2\":\"If we become aware that we have collected personal information from a child under 13 without parental consent, we will take steps to delete that information as soon as possible. If you believe we have collected information from a child under 13, please contact us immediately.\",\"text3\":\"Users between 13 and 18 should use the Site with the permission and supervision of a parent or legal guardian.\"},\"section3\":{\"title\":\"3. Legal Basis for Processing\",\"text1\":\"We process your personal data only where we have a lawful basis to do so. The basis we rely on for each type of processing is as follows:\",\"basis1\":\"Account creation \u0026 service delivery: Performance of a contract (necessary to provide the services you have requested)\",\"basis2\"
16:\"Payment processing \u0026 transaction records: Performance of a contract and Legal obligation (required for financial compliance)\",\"basis3\":\"Marketing communications: Consent (we will only send marketing emails where you have opted in)\",\"basis4\":\"Analytics \u0026 service improvement: Legitimate interests (we have a legitimate interest in understanding how our services are used to improve them, balanced against your privacy rights)\",\"basis5\":\"Fraud prevention \u0026 security: Legitimate interests and Legal obligation (we have a legitimate interest in protecting our platform and users)\",\"basis6\":\"Compliance with legal obligations: Legal obligation (required by applicable laws including Malaysia PDPA 2010, GDPR, and other applicable regulations)\",\"basis7\":\"AI chatbot interactions: Legitimate interests (processing is necessary to deliver the AI assistant feature you actively use)\",\"note\":\"Where we rely on legitimate interests, we have assessed that our interests are not overridden by your rights. You may object to processing based on legitimate interests at any time by contacting us.\"},\"section4\":{\"title\":\"4. Cookies and Tracking Technologies\",\"text1\":\"We use cookies and similar tracking technologies to facilitate website functionality, analyze site traffic, and deliver personalized content. Cookies are small data files stored on your device.\",\"typesTitle\":\"Types of Cookies We Use:\",\"item1\":\"Essential Cookies: Required for the Site to function (authentication, security)\",\"item2\":\"Analytics Cookies: Help us understand how users interact with the Site\",\"item3\":\"Functional Cookies: Remember your preferences and settings\",\"item4\":\"Marketing Cookies: Track your activity to deliver relevant advertisements\",\"text2\":\"You will be asked for c
16onsent before we place non-essential cookies. You can manage your cookie preferences through our banner or your browser settings. Blocking cookies may affect your experience on the Site.\",\"dnt\":\"Do Not Track: We currently do not respond to \\\"Do Not Track\\\" signals from browsers.\",\"googleSignals\":{\"title\":\"Google Analytics and Google Signals\",\"text1\":\"We use Google Analytics to analyze website traffic and user behavior. We have activated Google Signals, which allows Google Analytics to collect visitation information from our Site and associate it with Google information from accounts of signed-in users who have consented to this association for the purpose of ads personalization. This Google information may include end-user location, search history, YouTube history, and data from sites that partner with Google. This data is used to provide aggregated and anonymized insights into our users' cross-device behaviors.\",\"text2\":\"You can access and delete this data at any time via Google's \u003cactivityLink\u003eMy Activity\u003c/activityLink\u003e page. You can opt out of Google Analytics Advertising Features through Google Ads Settings, mobile ad settings, or by using the \u003coptOutLink\u003eGoogle Analytics Opt-out Browser Add-on\u003c/optOutLink\u003e. We and third-party vendors use first-party cookies (such as the Google Analytics cookie) and third-party cookies (such as Google advertising cookies) together to optimize and serve ads based on past visits to our Site.\"}},\"section5\":{\"title\":\"5. Use of Your Information\",\"text1\":\"We use the information collected to:\",\"item1\":\"Provide, operate, and maintain the Site and our courses\",\"item2\":\"Process your transactions and manage your orders\",\"item3\":\"Create and manage your account\",\"item4\":\"Track your course progress and issue certificates\",\"item5\":\"Send you transactional emails (order confirmations, password resets, account updates)\",\"item6\":\"Send marketing communications where you have opted in\",\"item7\":\"Respond to your comments, questions, and customer service requests\",\"item8\":\"Analyze usage patterns to improve our services and develop new features\",\"item9\":\"Detect, prevent, and address technical issues or fraud\",\"item10\":\"Comply with legal obligations and enforce our Terms and Conditions\",\"item11\":\"Personalize content delivery and user interface configuration based on user activity\"},\"section6\":{\"title\":\"6. Disclosure of Your Information\",\"text1\":\"We may share your information with:\",\"providers\":{\"title\":\"Service Providers: We work with third-party companies to provide services on our behalf:\",\"item1\":\"Hosting Providers: Cloud infrastructure and server hosting services\",\"item2\":\"Analytics Services: Tools to analyze website usage and user behavior\",\"item3\":\"Email Service Providers: Platforms for sending transactional and marketing emails\",\"item4\":\"Payment Processors: Secure payment processing services (they handle your payment data directly and securely)\",\"item5\":\"Customer Support Tools: Platforms to manage support tickets and customer communications\",\"item6\":\"AI/LLM API Providers: Chat messages you send to the AI assistant are transmitted to third-party large language model (LLM) API providers to generate responses. These providers may vary over time and are bound by data processing agreements. Messages are used solely to generate a response and are not used to train models beyond the provider's standard practices.\"},\"text2\":\"These service providers are contractually bound to protect your data and use it only for the purposes we specify.\",\"legal\":{\"title\":\"Legal Requirements: We may disclose your information if required by law, court order, or government request, or to:\",\"item1\":\"Comply with legal obligations\",\"item2\":\"Protect and defend our rights or property\",\"item3\":\"Prevent fraud or illegal activity\",\"item4\":\"Protect the safety of users or the public\"},\"transfers\":\"Business Transfers: If we are involved in a merger, acquisition, or sale of assets, your information may be transferred. You will be notified of any such change.\",\"consent\":\"With Your Consent: We may share your information for any other purpose with your explicit consent.\"},\"section7\":{\"title\":\"7. Your Rights Under GDPR (European Users)\",\"text1\":\"If you are located in the European Economic Area (EEA), you have the following rights under the General Data Protection Regulation (GDPR):\",\"item1\":\"Right of Access: Request a copy of the personal data we hold about you\",\"item2\":\"Right to Rectification: Request correction of inaccurate or incomplete data\",\"item3\":\"Right to Erasure: Request deleti
16on of your personal data (\\\"right to be forgotten\\\")\",\"item4\":\"Right to Restriction: Request that we limit how we use your data\",\"item5\":\"Right to Data Portability: Receive your data in a structured, machine-readable format\",\"item6\":\"Right to Object: Object to our processing of your data, particularly for marketing\",\"item7\":\"Right to Withdraw Consent: Withdraw consent at any time where we rely on consent\",\"item8\":\"Right to Lodge a Complaint: File a complaint with your local data protection authority\",\"contact\":\"To exercise any of these rights, please contact us. We will respond to your request within 30 days.\"},\"section8\":{\"title\":\"8. Your Rights Under CCPA/CPRA (California Users)\",\"text1\":\"If you are a California resident, you have the following rights under the California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA):\",\"item1\":\"Right to Know: Request information about the personal data we collect, use, and disclose\",\"item2\":\"Right to Delete: Request deletion of your personal data\",\"item3\":\"Right to Opt-Out: Opt-out of the sale of your personal data (we do not sell personal data)\",\"item4\":\"Right to Non-Discrimination: Exercise your privacy rights without discriminatory treatment\",\"item5\":\"Right to Correct: Request correction of inaccurate personal information\",\"item6\":\"Right to Limit: Limit the use and disclosure of sensitive personal information\",\"contact\":\"To exercise your rights, contact us. We will verify your identity before processing your request.\"},\"section9\":{\"title\":\"9. Data Security\",\"text1\":\"We implement appropriate technical and organizational security measures to protect your personal data from unauthorized access, disclosure, alteration, or destruction. These measures include:\",\"item1\":\"Encryption of data in transit using SSL/TLS\",\"item2\":\"Encryption of sensitive data at rest\",\"item3\":\"Regular security assessments and updates\",\"item4\":\"Access controls and authentication requirements\",\"item5\":\"Secure payment processing through PCI-DSS compliant providers\",\"item6\":\"Employee training on data protection\",\"text2\":\"However, no method of transmission over the Internet or electronic storage is 100% secure. While we strive to protect your personal data, we cannot guarantee absolute security. You are responsible for maintaining the confidentiality of your account credentials.\"},\"section10\":{\"title\":\"10. Data Breach Notification\",\"text1\":\"In the event of a data breach that is likely to result in a risk to your rights and freedoms, we will notify you and the relevant supervisory authorities within 72 hours of becoming aware of the breach, as required by law.\",\"text2\":\"The notification will include the nature of the breach, the categories of data affected, the likely consequences, and the measures we have taken or propose to take to address the breach and mitigate its effects.\"},\"section11\":{\"title\":\"11. Retention of Your Information\",\"text1\":\"We retain your personal data only for as long as necessary to fulfill the purposes outlined in this Privacy Policy, unless a longer retention period is required or permitted by law.\",\"periodsTitle\":\"Retention Periods:\",\"item1\":\"Account Data: Retained while your account is active and for 30 days after account deletion\",\"item2\":\"Course Progress: Retained while your account is active or as long as you have access to courses\",\"item3\":\"Transaction Records: Retained for 7 years to comply with tax and accounting regulations\",\"item4\":\"Marketing Data: Retained until you withdraw consent or 2 years of inactivity\",\"item5\":\"Technical Logs: Retained for 90 days for security and troubleshooting purposes\",\"text2\":\"After the retention period expires, we will securely delete or anonymize your data. You can request earlier deletion by contacting us, subject to legal obligations.\"},\"section12\":{\"title\":\"12. Account Deletion\",\"text1\":\"You may request deletion of your account at any time by contacting us. Upon receiving your request, we will:\",\"item1\":\"Deactivate your account within 48 hours\",\"item2\":\"Delete your personal data within 30 days\",\"item3\":\"Retain transaction records for legal compliance (anonymized where possible)\",\"item4\":\"Remove your access to purchased courses\",\"text2\":\"Please note that account deleti
16on is permanent and cannot be undone. You will lose access to all purchased courses and certificates.\"},\"section13\":{\"title\":\"13. International Data Transfers\",\"text1\":\"Your personal data may be transferred to and processed in countries outside of your country of residence, including countries that may not have the same level of data protection laws.\",\"text2\":\"When we transfer data from the EEA to countries without adequate protection, we implement appropriate safeguards such as:\",\"item1\":\"Standard Contractual Clauses (SCCs) approved by the European Commission\",\"item2\":\"Ensuring our service providers are certified under recognized frameworks\",\"item3\":\"Obtaining your explicit consent where required\",\"text3\":\"By using our Site, you acknowledge and consent to the transfer of your data as described in this policy.\"},\"section14\":{\"title\":\"14. Third-Party Links and Services\",\"text1\":\"The Site may contain links to third-party websites or services that are not operated by us. We have no control over and assume no responsibility for the content, privacy policies, or practices of any third-party sites or services.\",\"text2\":\"We strongly advise you to review the privacy policy of every site you visit. This Privacy Policy applies only to information collected through our Site.\"},\"section15\":{\"title\":\"15. Automated Decision-Making\",\"text1\":\"We may use automated systems to analyze your learning patterns and recommend relevant courses or content. These automated decisions do not have legal or significant effects on you. You have the right to request human review of any automated decision that affects you.\"},\"section16\":{\"title\":\"16. Marketing Communications\",\"text1\":\"Subject to your consent, we may transmit marketing communications regarding new courses, platform features, promotional offers, and related updates.\",\"text2\":\"You can unsubscribe from marketing communications at any time by:\",\"item1\":\"Clicking the \\\"unsubscribe\\\" link in any marketing email\",\"item2\":\"Updating your preferences in your account settings\",\"item3\":\"Contacting us directly\",\"text3\":\"Please note that you will continue to receive transactional emails (order confirmations, password resets, etc.) even if you opt out of marketing communications.\"},\"section17\":{\"title\":\"17. Changes to This Privacy Policy\",\"text1\":\"We may update this Privacy Policy from time to time to reflect changes in our practices, technology, legal requirements, or other factors. We will notify you of any material changes by:\",\"item1\":\"Posting the new Privacy Policy on this page with an updated \\\"Last Updated\\\" date\",\"item2\":\"Sending you an email notification if the changes are significant\",\"item3\":\"Displaying a prominent notice on the Site\",\"text2\":\"We encourage you to review this Privacy Policy periodically. Your continued use of the Site after any changes indicates your acceptance of the updated policy.\"},\"section18\":{\"title\":\"18. Contact Us and Data Protection\",\"text1\":\"If you have any questions, concerns, or requests regarding this Privacy Policy or our data practices, please contact us:\",\"item1\":\"Email: Through our contact form\",\"item2\":\"Data Protection Inquiries: For GDPR or data protection requests, please clearly mark your communication as \\\"Privacy Request\\\"\",\"text2\":\"We will respond to your inquiry within 30 days. For data subject requests under GDPR or CCPA, we will verify your identity before processing your request.\"},\"section19\":{\"title\":\"19. Malaysia Personal Data Protection Act (PDPA)\",\"text1\":\"APX Machine Learning Technologies is incorporated in Malaysia and processes personal data in accordance with the Personal Data Protection Act 2010 (PDPA). We comply with the seven data protection principles established under the PDPA: the General Principle, Notice and Choice Principle, Disclosure Principle, Security Principle, Retention Principle, Data Integrity Principle, and Access Principle.\",\"text2\":\"If you are a data subject whose personal data we process, you have the following rights under the PDPA:\",\"item1\":\"Right of Access: Request access to your personal data held by us\",\"item2\":\"Right to Correction: Request correction of inaccurate, incomplete, misleading, or outdated personal data\",\"item3\":\"Right to Withdraw Consent: Withdraw your consent to the processing of your personal data at any time, subject to legal and contractual restrictions\",\"item4\":\"Right to Limit Processing: Request that we limit the processing of your personal data to storage only\",\"text3\":\"To exercise any of these rights, please contact us through our contact form.\"}},\"meta\":{\"title\":\"Compute Efficiency - ApX Machine Learning\",\"description\":\"Right-sizing AI systems beats chasing scale. Learn to build efficie
16nt models that compete without the cloud bill.\"},\"title\":\"Engineering for Efficiency\",\"lastUpdated\":\"Last Updated: April 8, 2026\",\"intro\":{\"paragraph1\":\"AI infrastructure providers have a straightforward business model: the more compute you provision, the more they earn. The ML hype cycle reinforces this: every new benchmark is set by a larger model, every deployment guide defaults to the biggest GPU tier available. If you're a production engineer trying to ship something reasonable, nobody in that ecosystem has an incentive to help you do it cheaper.\",\"paragraph2\":\"We think right-sizing AI systems is an engineering discipline, not a compromise. This page explains how we think about resource tradeoffs on this platform, and why we teach engineers to apply the same reasoning to their own systems.\"},\"greenInfrastructure\":{\"title\":\"The Scaling Trap: Hardware Waste vs. Resource Optimization\",\"point1\":\"Teams routinely spin up A100 or H100 clusters for inference workloads that a quantized 7B model running on CPU would handle with acceptable latency. The GPU was available, the budget was there for the quarter, and nobody stopped to benchmark the alternative.\",\"point2\":\"The compute cost is visible. The less visible costs are: architectural complexity that slows iteration, cold-start latency from over-engineered orchestration, and the operational burden of maintaining infrastructure that was never the right fit.\",\"point3\":\"Over-provisioning limits availability. By optimizing for compute efficiency, we shift AI from an elite cloud resource to a highly portable system running on accessible local or hybrid hardware.\",\"armChart\":{\"title\":\"Processor Cost/Performance: ARM vs. x86 for Inference\",\"xAxisTitle\":\"Processor Type\",\"yAxisTitle\":\"Relative Energy Cost per Inference (%)\",\"x86Label\":\"x86 Traditional\",\"armLabel\":\"ARM-based\",\"reductionText\":\"72% cost reduction\"}},\"responsibleModelUsage\":{\"title\":\"Right-Sizing Is the Skill Nobody Teaches\",\"paragraph1\":\"The decision of which model to deploy is treated as a fixed input in most ML courses: you use the best available model, full stop. But for a production system, model selection is one of the highest-leverage engineering decisions you make. A 7B model with quantization running on a $0.50/hr instance versus a 70B model on a $12/hr GPU instance is not a minor implementation detail.\",\"paragraph2\":\"We teach engineers to reason about these tradeoffs explicitly: task complexity vs. model capacity, latency requirements vs. throughput, fine-tuning cost vs. prompt engineering cost. The goal is to match the tool to the problem, not to use whatever the leaderboard says is best.\",\"energyChart\":{\"title\":\"Relative Compute Cost by Training Approach\",\"xAxisTitle\":\"Approach\",\"yAxisTitle\":\"Relative Compute Cost (%)\",\"traditional\":\"Full Training from Scratch\",\"transfer\":\"Transfer Learning\",\"distillation\":\"Model Distillation\",\"edgeml\":\"Edge / Quantized Deployment\",\"reduction65\":\"65% cost reduction\",\"reduction75\":\"75% cost reduction\",\"reduction90\":\"90% cost reduction\"}},\"scalingAnxiety\":{\"title\":\"Scaling Anxiety Is Manufactured\",\"paragraph1\":\"There is a structural incentive in the AI industry to make you feel behind. Benchmark leaderboards reward parameter count. Conference talks default to the largest model the speaker could access. AI infrastructure providers publish reference architectures that assume you need a dedicated GPU cluster. None of these actors are lying to you; they're just optimizing for their own interests, which are not yours.\",\"paragraph2\":\"The result is what we call scaling anxiety: the pervasive sense that whatever compute you have is insufficient, and that the solution is always more. More parameters, more GPUs, more managed services, more spend. Teams overengineer because they've been trained to treat infrastructure size as a proxy for engineering quality.\",\"contrast\":{\"label\":\"The questions worth asking instead:\",\"items\":[\"Not: \\\"
16which model is the biggest?\\\" â But: \\\"which model is right-sized for this task?\\\"\",\"Not: \\\"how do I scale my GPU cluster?\\\" â But: \\\"how do I avoid needing one?\\\"\",\"Not: \\\"which cloud provider has the best H100 pricing?\\\" â But: \\\"does this task actually need a GPU?\\\"\",\"Not: \\\"how do I fine-tune the largest model?\\\" â But: \\\"what's the smallest model that solves this?\\\"\"]}},\"accessibleAI\":{\"title\":\"Compute Efficiency Is the Great Equalizer\",\"paragraph1\":\"The teams that have figured out efficiency (not by choice but by necessity) are often far ahead of well-funded teams that defaulted to scale. A quantized 7B model, fine-tuned on domain data, frequently matches a 70B API call at roughly 5% of the cost. That gap is not temporary; it reflects a fundamental mismatch between what frontier models are optimized for and what most production tasks actually require.\",\"paragraph2\":\"This matters globally. Most of the world's engineers are building AI under meaningful hardware and budget constraints. Efficiency techniques aren't a workaround; they're the main path. We think that audience deserves better resources than \\\"here's how to call the API.\\\"\"},\"sustainableEducation\":{\"title\":\"What We Actually Teach\",\"paragraph1\":\"Our courses cover quantization (INT4, INT8, GPTQ, AWQ), knowledge distillation, LoRA and QLoRA fine-tuning, and edge inference, not because these are niche topics, but because they are the skills that let a two-person team compete with one that has a $500k cloud budget. We also cover when not to use these techniques, because blindly applying them to the wrong problem is its own failure mode.\",\"paragraph2\":\"The broader engineering judgment (when to reach for a frontier model, when to fine-tune a smaller one, or when to skip ML entirely) is baked into how we structure content. We think that judgment is more valuable than knowing which model scored highest on a benchmark last Tuesday.\",\"lifecycle\":{\"problem\":\"Production\\\\nProblem\",\"selection\":\"Model Selection\\\\n\u0026 Tradeoffs\",\"technique\":\"Apply Efficient\\\\nTechnique\",\"outcome\":\"Lower Cost\\\\nSame Results\",\"assess\":\"Assess\",\"choose\":\"Choose\",\"deploy\":\"Deploy\",\"next\":\"Next Problem\"}},\"carbonFootprint\":{\"title\":\"The Power and Thermal Overhead of Unoptimized Deployments\",\"paragraph1\":\"Power consumption scales directly with compute utilization, directly impacting operational overhead, cooling requirements, and hardware longevity. Training a 175B parameter model requires orders of magnitude more raw energy than a right-sized 7B model. This cost overhead remains high even when utilizing subsidized or offset-powered data centers.\",\"paragraph2\":\"Optimizing resource utilization is not about sacrificing performance. A right-sized model typically achieves comparable task accuracy while running at a fraction of the hardware cost, eliminating the need to maintain complex multi-node cluster orchestration for simple inference workloads.\",\"chart\":{\"title\":\"Estimated Training Power Overhead by Model Size\",\"xAxisTitle\":\"Model Size\",\"yAxisTitle\":\"Energy Consumption (MWh, log scale)\",\"small\":\"Small Model\\n(~1B params)\",\"medium\":\"Medium Model\\n(~7B params)\",\"large\":\"Large Model\\n(~70B params)\",\"massive\":\"Massive Model\\n(~175B params)\"}},\"responsiblePractices\":{\"title\":\"How We Run This Platform\",\"paragraph1\":\"This platform runs on ARM-based infrastructure. We use smaller, task-appropriate models for internal tooling and content pipelines, not frontier models where a smaller one does the job. Caching is a first-class concern, not an afterthought. We don't provision for peak capacity by default.\",\"paragraph2\":\"We document these choices not as a PR exercise but because they reflect the tradeoffs we teach. If we recommended right-sizing to our users while running overprovisioned infrastructure ourselves, that would be a position worth questioning.\",\"lifecycleTitle\":\"Right-Sizing Decision Lifecycle\",\"lifecycle\":{\"input\":\"Define Task\\\\nRequirements\",\"selection\":\"Model Selection\",\"complexity\":\"Task Complexity\\\\nAssessment\",\"latency\":\"Latency vs\\\\nThroughput\",\"budget\":\"Cost / Infra\\\\nConstraints\",\"techniques\":\"Efficiency Techniques\",\"quantization\":\"Quantization\\\\n(INT4/INT8/GPTQ)\",\"finetuning\":\"LoRA / QLoRA\\\\nFine-tuning\",\"distillation\":\"Knowledge\\\\nDistillation\",\"deployment\":\"Deployment\",\"edge\":\"Edge\\\\nInference\",\"cache\":\"Caching\\\\nStrategy\",\"monitoring\":\"Latency \u0026\\\\nCost Monitoring\",\"outcome\":\"Right-sized\\\\nProduction System\",\"reevaluate\":\"Re-evaluate\"}},\"ongoingCommitment\":{\"title\":\"Ongoing Commitment\",\"paragraph1\":\"These aren't fixed positions. Model efficiency research is moving fast; techniques that required significant engineering overhead a year ago are becoming standard library features. We update our courses and our own infrastru
16cture choices as the landscape shifts.\",\"paragraph2\":\"If you've found a better approach to a tradeoff we've described here, or disagree with our reasoning on a specific point, we're interested. The goal is to get this right, not to look like we have.\"},\"TermsPage\":{\"title\":\"Terms and Conditions\",\"lastUpdated\":\"Last Updated: June 17, 2026\",\"intro\":\"Welcome to ApX Machine Learning, a service provided by APX MACHINE LEARNING TECHNOLOGIES (Registration No: 202603008008 (003810963-D)), a company incorporated in Malaysia (\\\"we\\\", \\\"our\\\", \\\"us\\\"). By accessing or using our website, you agree to comply with and be bound by the following terms and conditions (\\\"Terms\\\"). Please review them carefully. If you do not agree with these Terms, you should not use our website.\",\"section1\":{\"title\":\"1. Acceptance of Terms\",\"text\":\"By accessing and using the Site, you accept and agree to be bound by these Terms and our Privacy Policy. If you do not agree to these Terms, you must not use or access the Site.\"},\"section2\":{\"title\":\"2. Changes to Terms\",\"text\":\"We reserve the right to modify or replace these Terms at any time. Any changes will be effective immediately upon posting on the Site. Your continued use of the Site following the posting of any changes constitutes acceptance of those changes.\"},\"section3\":{\"title\":\"3. Eligibility\",\"text\":\"Our courses and services are intended for individuals aged 13 and older. Users in the European Union must be at least 16 years of age, or have verifiable parental or guardian consent, in accordance with Article 8 of the GDPR. If you are under 18, you should use the Site with permission from a parent or guardian. We do not knowingly collect personal data from children under 13 (or under 16 in the EU). If we become aware that we have collected such data without appropriate consent, we will take steps to delete it promptly.\"},\"section4\":{\"title\":\"4. User Accounts\",\"text\":\"To access certain features of the Site, you may be required to create an account. You agree to provide accurate, current, and complete information during the registration process and to update such information to keep it accurate, current, and complete. You are responsible for safeguarding your password and for any activities or actions under your account. Each account, subscription, one-time pass, and API key is licensed strictly for individual use by a single designated natural person. You may not share your account credentials, login tokens, or API access with other individuals, team members, or third parties. Multi-tenant access, credential pooling, or concurrent use of a single account across multiple users is strictly prohibited. We reserve the right to suspend or terminate accounts without refund if credential sharing or unauthorized multi-user access is detected.\"},\"section5\":{\"title\":\"5. Use of the Site\",\"text\":\"You agree to use the Site only for lawful purposes and in accordance with these Terms. You agree not to:\",\"item1\":\"Use the Site in any way that violates any applicable federal, state, local, or international law or regulation.\",\"item2\":\"Engage in any conduct that restricts or inhibits anyone's use or enjoyment of the Site, or which, as determined by us, may harm us or users of the Site or expose them to liability.\",\"item3\":\"Use the Site in any manner that could disable, overburden, damage, or impair the Site or interfere with any other party's use of the Site.\",\"item4\":\"Use the Site on behalf of, or for the benefit of, any third party, or incorporate access to the Site as part of any managed, supervised, or structured programme without our prior written agreement.\"},\"section6\":{\"title\":\"6. Intellectual Property Rights\",\"text\":\"The Site and its entire contents, features, and functionality are owned by APX MACHINE LEARNING TECHNOLOGIES or its licensors and are protected by intellectual property laws.\"},\"section7\":{\"title\":\"7. Payment and Pricing\",\"text1\":\"Certain courses and services on the Site may require payment. All prices are displayed in the applicable currency and are subject to change without notice. You agree to pay all fees and charges incurred in connection with your account at the prices in effect when such charges are incurred.\",\"text2\":\"Payment must be made through the payment methods we accept. By providing payment information, you represent and warrant that you are authorized to use the designated payment method and authorize us to charge your payment method for the total amount of your purchase.\"},\"section8\":{\"title\":\"8. Refund Policy\",\"text1\":\"We offer a 7-day money-back guarantee for paid courses, provided that you have not attempted a Project submission. If you meet the criteria and are not satisfied, you may request a full refund within 7 days of purchase by contacting us.\",\"text2\":\"Once a course Project attempt has been submitted for review or a Certificate of Completion has been issued, the sale is considered final and n
16on-refundable. After the 7-day window, all sales are final and non-refundable.\",\"text3\":\"For subscription plans, you may cancel your subscription at any time. Cancellation will take effect at the end of the current billing period and you will retain access until that date. Subscription fees already paid are non-refundable unless otherwise required by applicable law. We do not offer pro-rated refunds for unused portions of a subscription period.\",\"text4\":\"Refunds will be processed to the original payment method within 5-10 business days. We reserve the right to refuse refunds if we detect abuse of our refund policy, including but not limited to users who repeatedly purchase and refund courses.\"},\"section9\":{\"title\":\"9. Course Access and Content\",\"text1\":\"Upon purchase of a course, you will receive access to that course content for as long as the platform remains in operation, subject to these Terms. \\\"Lifetime access\\\" refers to the life of the platform and not a guaranteed perpetual right. Course access is personal to you and may not be shared, transferred, resold, or used on behalf of any other person or organisation.\",\"text2\":\"You may download course materials for personal use only. You agree not to:\",\"item1\":\"Share, distribute, or resell course content in any format\",\"item2\":\"Use course content for commercial purposes without our written permission\",\"item3\":\"Remove or modify any copyright notices or proprietary markings\",\"item4\":\"Upload course content to file-sharing or public platforms\",\"item5\":\"Use course access as part of any organised group learning activity, managed training programme, or structured internal curriculum, whether or not a fee is charged to participants.\",\"text3\":\"We reserve the right to modify, update, or discontinue any course at any time. While we strive to maintain access to purchased courses, we do not guarantee perpetual availability of any specific content. Systematic or bulk access to the Site, including managed enrollment or coordinated access by multiple users under a common organisational purpose, requires a separate written agreement with us.\"},\"section10\":{\"title\":\"10. Certificates\",\"text\":\"Upon successful completion of certain courses, you may receive a certificate of completion. Certificates are provided for educational purposes only and do not constitute professional certification, accreditation, or qualification. We are not responsible for how third parties interpret or accept our certificates.\"},\"section11\":{\"title\":\"11. Marketing Communications\",\"text\":\"Where you have given consent, we may send you marketing emails. You can unsubscribe at any time by clicking the \\\"unsubscribe\\\" link in our emails or by contacting us.\"},\"section12\":{\"title\":\"12. User-Generated Content\",\"text1\":\"If you post, upload, or submit any content to the Site (including comments, forum posts, or reviews), you grant us a worldwide, non-exclusive, royalty-free license to use, reproduce, modify, and display such content in connection with operating the Site.\",\"text2\":\"You represent that you own or have the necessary rights to any content you submit. We reserve the right to remove any user-generated content that violates these Terms or is otherwise objectionable at our sole discretion.\"},\"section13\":{\"title\":\"13. Data Protection\",\"text\":\"Your personal data will be processed in accordance with our Privacy Policy. APX Machine Learning Technologies complies with the Personal Data Protection Act 2010 (PDPA) of Malaysia. If you are located in the European Union, we additionally handle your data in compliance with the General Data Protection Regulation (GDPR). If you are a resident of California, USA, you may have additional rights under the California Consumer Privacy Act (CCPA/CPRA). For details on your rights and how to exercise them, please refer to our Privacy Policy. No data processing agreement (DPA) is provided under these Terms. Users requiring a DPA for regulatory or organisational compliance purposes must contact us separately to arrange a written agreement before using the Site in any capacity subject to such requirements.\"},\"section14\":{\"title\":\"14. Disclaimers and Warranties\",\"text1\":\"THE SITE AND ALL COURSES, CONTENT, AND SERVICES ARE PROVIDED \\\"AS IS\\\" AND \\\"AS AVAILABLE\\\" WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT.\",\"text2\":\"We do not warrant that:\",\"item1\":\"The Site will be uninterrupted, timely, secure, or error-free\",\"
16item2\":\"Course content will meet your specific requirements or expectations\",\"item3\":\"You will achieve any particular learning outcome or career result\",\"item4\":\"Any errors or defects will be corrected\",\"text3\":\"We do not provide professional engineering or procurement advice under these Terms; any such advisory or consulting services require a separate written agreement. All compatibility and pricing estimates are approximations, and you are responsible for verifying all specifications independently before making financial or purchasing decisions. No service level agreement (SLA), uptime guarantee, or formal support commitment is provided under these Terms.\"},\"section15\":{\"title\":\"15. Limitation of Liability\",\"text1\":\"TO THE MAXIMUM EXTENT PERMITTED BY LAW, IN NO EVENT SHALL APX MACHINE LEARNING TECHNOLOGIES, ITS OFFICERS, DIRECTORS, EMPLOYEES, OR AGENTS BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, CONSEQUENTIAL, OR PUNITIVE DAMAGES, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, DATA, USE, OR OTHER INTANGIBLE LOSSES, ARISING FROM:\",\"item1\":\"Your access to or use of (or inability to access or use) the Site\",\"item2\":\"Any conduct or content of any third party on the Site\",\"item3\":\"Unauthorized access, use, or alteration of your content\",\"item4\":\"Any content obtained from the Site\",\"text2\":\"IN NO EVENT SHALL OUR TOTAL LIABILITY TO YOU FOR ALL DAMAGES, LOSSES, OR CAUSES OF ACTION EXCEED THE AMOUNT YOU HAVE PAID TO US IN THE LAST TWELVE (12) MONTHS, OR ONE HUNDRED DOLLARS ($100), WHICHEVER IS GREATER.\"},\"section16\":{\"title\":\"16. Indemnification\",\"text\":\"You agree to defend, indemnify, and hold harmless APX MACHINE LEARNING TECHNOLOGIES and its officers, directors, employees, and agents from and against any claims, liabilities, damages, losses, and expenses, including reasonable attorneys' fees, arising out of or in any way connected with:\",\"item1\":\"Your access to or use of the Site\",\"item2\":\"Your violation of these Terms\",\"item3\":\"Your violation of any third-party rights\",\"item4\":\"Any content you submit to the Site\"},\"section17\":{\"title\":\"17. Termination\",\"text1\":\"We may terminate or suspend your access to the Site and your account immediately, without prior notice or liability, for any reason, including if you breach these Terms. Upon termination, your right to use the Site will immediately cease.\",\"text2\":\"If your account is terminated for cause, you will not be entitled to any refund of unused fees. Sections of these Terms that by their nature should survive termination shall survive, including but not limited to ownership provisions, warranty disclaimers, indemnity, and limitations of liability.\"},\"section18\":{\"title\":\"18. Dispute Resolution and Arbitration\",\"text1\":\"Any dispute arising out of or relating to these Terms or the Site shall first be resolved through good faith negotiations. If the dispute cannot be resolved through negotiation within 30 days, it shall be resolved through binding arbitration conducted in Malaysia in accordance with the Asian International Arbitration Centre (AIAC) Arbitration Rules.\",\"text2\":\"Where permitted by applicable law, you agree that any arbitration or legal proceeding shall be conducted on an individual basis and not as a class action or collective action. If you are located in the European Union or another jurisdiction where class action waivers or mandatory arbitration are not enforceable by law, this clause shall not apply to you and you retain the right to bring claims before the competent courts of your jurisdiction.\"},\"section19\":{\"title\":\"19. Governing Law and Jurisdiction\",\"text\":\"These Terms shall be governed by and construed in accordance with the laws of Malaysia, without regard to its conflict of law provisions. You agree to submit to the exclusive jurisdiction of the courts located in Malaysia for the resolution of any disputes.\"},\"section20\":{\"title\":\"20. Severability\",\"text\":\"If any provision of these Terms is held to be invalid, illegal, or unenforceable, the validity, legality, and enforceability of the remaining provisions shall not be affected or impaired in any way.\"},\"section21\":{\"title\":\"21. Entire Agreement\",\"text\":\"These Terms, together with our Privacy Policy and any other legal notices published by us on the Site, constitute the entire agreement between you and APX MACHINE LEARNING TECHNOLOGIES regarding your use of the Site and supersede all prior agreements and understandings.\"},\"section22\":{\"title\":\"22. Assignment\",\"text\":\"You may not assign or transfer these Terms or any rights or obligations hereunder without our prior written consent. We may assign or transfer these Terms without restriction.\"},\"section23\":{\"title\":\"23. Force Majeure\",\"text\":\"We shall not be liable for any failure or delay in performance due to circumstances beyond our reasonable control, including acts of God, war, terrorism, riots, natural disasters, or failure of telecommunications or Internet services.\"},\"section24\":{\"title\":\"24. Updates to Terms\",\"text\":\"We may update these Terms from time to time. When we do, we will revise the \\\"Last Updated\\\" date at the top of this page and, where the changes are significant, notify you by email or a prominent notice on the Site.\",\"footer\":\"We encourage you to review these Terms periodically. Your continued use of the Site after any changes indicates your acceptance of the updated Terms.\"},\"section25\":{\"title\":\"25. Contact Us\",\"text\":\"If you have any questions about these Terms, please contact us.\"}},\"TransparencyPage\":{\"title\":\"Content Integrity \u0026 Transparency Policy\",\"lastUpdated\":\"Last Updated: February 4, 2026\",\"founderNote\":{\"title\":\"A Note from the Founder\",\"text1\":\"ApX Machine Learning began as a personal project, a collection of tools and notes I built for my own use to help me develop practical skills in AI/ML. As the platform has grown, gaining significant traction and scrutiny from the global technical community, there comes a responsibility to be as transparent as possible about how the content is created, verified, and delivered.\",\"text2\":\"Today, as the content scale has grown through Agentic Workflows, the resulting materials have become almost indistinguishable to most from human-authored work. This high level of refinement, combined with the technical depth, has unintentionally positioned this platform as a \u003chighFidelityLink\u003ehigh-fidelity data source for Large Language Models\u003c/highFidelityLink\u003e.\",\"text2Tooltip\":\"In other words, a training data goldmine\",\"text3\":\"Given this context, I believe it is important to openly share my approach to AI ethics, content integrity, and the specific ways I utilize generative AI in my workflow. In fact, LLM bot traffic now outweighs human traffic by a significant margin. Recognizing that the content here now has a direct influence on the output of major LLMs, there is an even greater responsibility to be transparent and precise. While it is possible to block these automated crawlers, I have chosen to maintain open access so that as many people (and models) as possible can continue to receive value from the content.\",\"text4\":\"This platform practices what it teaches. The techniques taught here, from agentic AI systems to model orchestration, are the same ones powering the creation and delivery of this content. ApX
16is both an educational resource and a working example of applied machine learning.\",\"author\":\"- Wei-Ming\"},\"section1\":{\"title\":\"1. Commitment to Technical Integrity\",\"text\":\"At \u003csiteName\u003eApX Machine Learning (apxml.com)\u003c/siteName\u003e, the mission is to provide practical, high-impact machine learning education. While AI is leveraged to enhance the workflow, the core value remains technical integrity. The objective is to ensure that theoretical concepts and practical applications are grounded in reality and suitable for educational use. More details can be found about how lessons are architected on the \u003caboutLink\u003eAbout Page\u003c/aboutLink\u003e.\"},\"section2\":{\"title\":\"2. Usage of Generative AI\",\"intro\":\"Artificial Intelligence is used as a \\\"force multiplier\\\" in the following ways:\",\"item1\":\"\u003cb\u003eDrafting \u0026 Structure:\u003c/b\u003e To organize complex topics into digestible lesson plans.\",\"item2\":\"\u003cb\u003eCreative Assets:\u003c/b\u003e To generate illustrative diagrams or headers that simplify abstract ML concepts.\",\"item3\":\"\u003cb\u003eCode Optimization:\u003c/b\u003e To suggest alternative syntax or refactoring patterns for educational comparison.\",\"item4\":\"\u003cb\u003eAssessment \u0026 Evaluation:\u003c/b\u003e To draft quiz questions and verify answers for curriculum assessments.\",\"item5\":\"\u003cb\u003eCoding \u0026 Scripting Assistance:\u003c/b\u003e To accelerate the development of platform features and internal automation scripts.\"},\"section3\":{\"title\":\"3. Content Stewardship \u0026 Verification\",\"intro\":\"AI assists the workflow, but engineering judgment provides the direction.\",\"item1\":\"\u003cb\u003eTechnical Review:\u003c/b\u003e Verification of code snippets, mathematical formulas, and benchmark results is prioritized. A combination of automated testing and periodic audits is used to maintain content quality despite the complexity of agentic pipelines.\",\"item2\":\"\u003cb\u003eNo \\\"Black Box\\\" Content:\u003c/b\u003e Raw AI outputs are not published. All synthetic text is refined for tone, technical accuracy, language nuance, and educational clarity.\",\"item3\":\"\u003cb\u003eAddressing Hallucinations:\u003c/b\u003e AI is not infallible. Despite exhaustive efforts to provide relevant citations and context, technical hallucinations can still occur. Every effort is made to reduce these errors, but they may still exist despite high intensity review.\",\"item4\":\"\u003cb\u003eBias Mitigation:\u003c/b\u003e The curriculum is actively audited to ensure AI tools do not introduce algorithmic bias into training materials.\"},\"section4\":{\"title\":\"4. Intellectual Property \u0026 Ownership\",\"item1\":\"\u003cb\u003eContent Rights:\u003c/b\u003e All course content, even if AI-assisted, is the intellectual property of apxml.com.\",\"item2\":\"\u003cb\u003eUser Privacy:\u003c/b\u003e Student data or private project submissions are not used to train external Large Language Models (LLMs) without explicit, opt-in consent.\"},\"section5\":{\"title\":\"5. Orchestration \u0026 Human-AI Collaboration\",\"intro\":\"The production of content involves complex orchestration, a multi-step process where specialized AI agents and human creators interact. This complexity introduces unique operational risks:\",\"item1\":\"\u003cb\u003eSystemic Complexity:\u003c/b\u003e Errors may arise not just from AI \\\"hallucinations,\\\" but from orchestration failures, such as a specific computational routine not being executed, a data transformation step failing, or a manual audit being bypassed in a complex sequence of events.\",\"item2\":\"\u003cb\u003eHuman Oversight:\u003c/b\u003e While human-in-the-loop is the standard, human error is an inherent part of any engineering process. Fatigue, oversight, or technical debt in internal tools can lead to imperfections.\",\"item3\":\"\u003cb\u003eInfrastructure Reliability:\u003c/b\u003e As Agentic workflows scale, the orchestration layer between different models and tools can occasionally break, leading to inaccuracies that are tirelessly patched.\"},\"section6\":{\"title\":\"6. Feedback \u0026 Accountability\",\"text\":\"If a technical hallucination, a bug, or an error in materials is identified, please report it via the \u
16003ccontactLink\u003eContact Page\u003c/contactLink\u003e. Feedback is essential in helping maintain the high standard of precision that the technical community expects.\"},\"section7\":{\"title\":\"7. Environmental Impact\",\"text1\":\"AI-assisted content creation requires substantial computational resources, and this platform is mindful of the environmental impact of large-scale model orchestration.\",\"text2\":\"Optimization efforts include:\",\"item1\":\"\u003cb\u003eEfficient Compute Architecture:\u003c/b\u003e Use of energy-efficient cloud infrastructure, including ARM-based processors that reduce power consumption per compute cycle.\",\"item2\":\"\u003cb\u003eModel Selection:\u003c/b\u003e Smaller, task-specific models are preferred over frontier models when educational quality is not compromised.\",\"item3\":\"\u003cb\u003eCaching \u0026 Batching:\u003c/b\u003e Aggressive caching of repetitive operations and batching of similar requests to minimize redundant LLM generations.\",\"text3\":\"However, there are inherent trade-offs. Agentic workflows with multi-step verification require multiple model calls to maintain content accuracy. The choice is made to prioritize technical precision over minimal compute, a single deeply verified lesson may consume more resources than a lightly edited alternative, but reduces the risk of spreading misinformation to both human learners and AI systems that may ingest this content.\",\"text4\":\"This is transparency over perfection. While optimization is a priority, this platform cannot claim carbon neutrality, and acknowledges that educational content creation at scale has an environmental cost.\"},\"section8\":{\"title\":\"8. Community Contributions \u0026 Feedback\",\"text1\":\"Community feedback is essential to maintaining content accuracy and improving educational clarity. Technical corrections, broken links, outdated information, and suggestions for improvement are welcome.\",\"text2\":\"The review process includes:\",\"item1\":\"\u003cb\u003eTechnical Validation:\u003c/b\u003e All submissions undergo review for technical accuracy before incorporation.\",\"item2\":\"\u003cb\u003eHuman-in-the-Loop:\u003c/b\u003e While AI assists in triaging feedback, final decisions on content changes are made by human reviewers.\",\"item3\":\"\u003cb\u003eScope:\u003c/b\u003e Corrections addressing factual errors, code bugs, or broken functionality are prioritized. Full course proposals or promotional content are outside the scope of community contributions.\",\"text3\":\"Corrections can be submitted via the \u003ccontactLink\u003eContact Page\u003c/contactLink\u003e with clear references to the specific lesson or page.\",\"text4\":\"Attribution for significant contributions is actively being developed. The goal is to recognize community members who help improve the platform while respecting those who prefer to remain anonymous.\"}},\"PythonSandbox\":{\"title\":\"Python Lab\",\"starterCode\":{\"comment\":\"Code here\",\"helloMessage\":\"Hello, Python!\",\"sineWaveTitle\":\"Sine wave\"},\"tooltips\":{\"librariesLoaded\":\"Scientific stack loaded (numpy, pandas, matplotlib)\",\"enableLibraries\":\"Enable scientific stack: numpy, pandas, matplotlib\",\"loadPackages\":\"Load Python packages\",\"closeLab\":\"Close lab\",\"clearOutput\":\"Clear output\",\"signupRequired\":\"Sign up for unlimited access to run code\",\"writePythonCode\":\"Write Python code\",\"copyCode\":\"Copy code\",\"codeCopied\":\"Copied!\",\"runInLab\":\"Run in Python Lab\"},\"buttons\":{\"run\":\"Run\",\"python\":\"Python\"},\"output\":{\"label\":\"Output\",\"loadingEnvironment\":\"Loading environment\",\"emptyState\":\"Run your code to see output here.\",\"noOutput\":\"(no output)\",\"imageAlt\":\"matplotlib output\"},\"errors\":{\"unknownError\":\"Unknown error initialising Pyodide.\",\"workerError\":\"Worker error.\"},\"packageSelector\":{\"modalTitle\":\"Load Python Packages\",\"tabs\":{\"quickGroups\":\"Quick Groups\",\"browseAll\":\"Browse All\"},\"quickGroupsDescription\":\"Select a pre-configured package group for common use cases:\",\"browseDescription\":\"Browse packages by category:\",\"loaded\":\"Loaded\",\"select\":\"Select\",\"packagesSelected\":\"{count, plural, =1 {# package selected} other {# packages selected}}\",\"cancel\":\"Cancel\",\"loadPackages\":\"Load Packages\",\"loading\":\"Loading...\"},\"packages\":{\"packagesLoaded\":\"{count, plural, =1 {# package loaded} other {# packages loaded}}\",\"loadingPackages\":\"Loading packages\",\"noPackagesSelected\":\"No packages selected\",\"selected\":\"selected\",\"load\":\"Load\",\"loaded\":\"Loaded\",\"selectedBadge\":\"Selected\",\"unselect\":\"Unselect\",\"select\":\"Select\"},\"shortcuts\":{\"title\":\"Keyboard shortcuts:\",\"runCode\":\"Run code\",\"runCodeAlt\":\"Run code (alternative)\"}
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Required if gpu_key is \u003cc1\u003ecustom_discrete\u003c/c1\u003e or \u003cc2\u003ecustom_apple_silicon\u003c/c2\u003e.\",\"customBandwidth\":\"Custom GPU memory bandwidth in GB/s. Required if gpu_key is \u003cc1\u003ecustom_discrete\u003c/c1\u003e or \u003cc2\u003ecustom_apple_silicon\u003c/c2\u003e.\",\"customTflops\":\"Custom GPU FP16 compute performance in TFLOPS. Required if gpu_key is \u003cc1\u003ecustom_discrete\u003c/c1\u003e or \u003cc2\u003ecustom_apple_silicon\u003c/c2\u003e.\",\"numGpus\":\"Number of GPUs distributed on a single node. Defaults to 1 when cluster parameters are not used; mutually exclusive with \u003cc1\u003enum_nodes\u003c/c1\u003e and \u003cc2\u003egpus_per_node\u003c/c2\u003e.\",\"batchSize\":\"Batch size. Default is 1.\",\"seqLength\":\"Context sequence length in tokens. Default is 2048.\",\"concurrentUsers\":\"Number of concurrent users. Default is 1.\",\"enablePrefixCaching\":\"Enable KV cache prefix caching. Defaults to the framework preset's behavior (on for \u003cc1\u003evllm\u003c/c1\u003e and \u003cc1\u003etensorrt_llm\u003c/c1\u003e). Takes effect with \u003cc1\u003eshared_prefix_ratio\u003c/c1\u003e above 0.\",\"sharedPrefixRatio\":\"Fraction of prompt sequence that is a shared prefix. Range: 0.0 to 1.0. Default is 0.0.\",\"enableContinuousBatching\":\"Enable continuous batching. Defaults to the framework preset's behavior.\",\"inferenceParallelism\":\"Parallelism strategy. Choices: \u003cc1\u003epipeline\u003c/c1\u003e, \u003cc2\u003etensor\u003c/c2\u003e. Default is \u003cc3\u003epipeline\u003c/c3\u003e.\",\"interconnectType\":\"Interconnect type for multi-GPU setups. Supported values: PCIe (\u003cc1\u003epcie3\u003c/c1\u003e, \u003cc1\u003epcie4\u003c/c1\u003e, \u003cc1\u003epcie5\u003c/c1\u003e), NVLink (\u003cc2\u003envlink_bridge\u003c/c2\u003e, \u003cc2\u003envlink_gen2\u003c/c2\u003e to \u003cc2\u003envlink_gen6\u003c/c2\u003e), InfiniBand (\u003cc1\u003einfiniband_hdr\u003c/c1\u003e, \u003cc1\u003einfiniband_edr\u003c/c1\u003e), and Ethernet (\u003cc1\u003eethernet10g\u003c/c1\u003e to \u003cc1\u003eethernet400g\u003c/c1\u003e). Default is \u003cc1\u003epcie4\u003c/c1\u003e.\",\"enableOffloading\":\"Enable GPU offloading. Default is \u003cc1\u003efalse\u003c/c1\u003e.\",\"offloadTarget\":\"Storage target for offloaded layers. Choices: \u003cc1\u003ecpu_ram\u003c/c1\u003e, \u003cc2\u003envme\u003c/c2\u003e.\",\"numOffloadLayers\":\"Number of model layers to offload.\",\"percentageOffload\":\"Percentage of layers to offload, from 0 to 100.\",\"offloadKvCache\":\"Offload KV Cache to storage target. Default is \u003cc1\u003efalse\u003c/c1\u003e.\",\"numNodes\":\"Number of physical server chassis/nodes in the cluster. Required when using cluster configuration; mutually exclusive with \u003cc1\u003enum_gpus\u003c/c1\u003e.\",\"gpusPerNode\":\"Number of GPUs populated per server node in the cluster. Required when using cluster configuration; mutually exclusive with \u003cc1\u003enum_gpus\u003c/c1\u003e.\",\"tpDegree\"
16:\"Tensor Parallelism (TP) degree/shards to partition weights across GPUs. Default is 1.\",\"ppDegree\":\"Pipeline Parallelism (PP) degree/shards to distribute model layers sequentially across nodes/devices. Default is 1.\",\"interNodeInterconnect\":\"Network connection interconnect between different nodes in the cluster. E.g., \u003cc1\u003eethernet100g\u003c/c1\u003e, \u003cc2\u003eethernet400g\u003c/c2\u003e, \u003cc3\u003einfiniband_hdr\u003c/c3\u003e.\",\"finetuningMethod\":\"Finetuning method. Choices: \u003cc1\u003efull\u003c/c1\u003e, \u003cc2\u003elora\u003c/c2\u003e, \u003cc3\u003eqlora\u003c/c3\u003e. Default is \u003cc4\u003elora\u003c/c4\u003e.\",\"fineTuningQuantization\":\"Base model precision for full finetuning. Used when method is \u003cc1\u003efull\u003c/c1\u003e. Choices: \u003cc2\u003efp32\u003c/c2\u003e, \u003cc3\u003efp16\u003c/c3\u003e, \u003cc4\u003efp8\u003c/c4\u003e. Default is \u003cc5\u003efp16\u003c/c5\u003e.\",\"loraRank\":\"LoRA rank size. Used when method is \u003cc1\u003elora\u003c/c1\u003e or \u003cc2\u003eqlora\u003c/c2\u003e. Default is 16.\",\"gradientAccumulationSteps\":\"Gradient accumulation steps. Default is 1.\",\"globalBatchSize\":\"Global batch size. Default is 1.\",\"numSamples\":\"Dataset size in total samples.\",\"tokensPerSample\":\"Average tokens per sample. Defaults to seq_length.\",\"numEpochs\":\"Number of training epochs. Default is 3.\",\"optimizationConfig\":\"Advanced optimization flags object. Contains boolean flags like \u003cc1\u003eflash_attention\u003c/c1\u003e, \u003cc2\u003egradient_checkpointing\u003c/c2\u003e, \u003cc3\u003euse_8bit_optimizer\u003c/c3\u003e, \u003cc4\u003euse_paged_optimizer\u003c/c4\u003e, \u003cc5\u003euse_fused_kernels\u003c/c5\u003e, \u003cc6\u003eactivation_offloading\u003c/c6\u003e, etc., and integer zero_stage.\",\"enableOffloadingFinetuning\":\"Enable offloading to host system. Default is \u003cc1\u003efalse\u003c/c1\u003e.\",\"vocabSize\":\"Custom vocabulary size (e.g., \u003cc1\u003e128256\u003c/c1\u003e for Llama 3, \u003cc2\u003e256000\u003c/c2\u003e for Gemma). Default is 32000.\",\"ffnIntermediateSize\":\"Custom MLP/SwiGLU intermediate dimension size (e.g., \u003cc1\u003e14336\u003c/c1\u003e for Llama 3 8B).\",\"kvSourceLayerCount\":\"Number of layers generating shared KV cache states for compressed or hybrid architectures like CSA2.\",\"kvLatentDim\":\"Latent compressed KV cache dimension for architectures like MLA or CSA2.\",\"indexerDim\":\"Sparse routing indexer key dimension for architectures like DSA or CSA2.\",\"frameworkPresetInference\":\"Runtime the estimate models: \u003cc1\u003eauto\u003c/c1\u003e, \u003cc1\u003evllm\u003c/c1\u003e, \u003cc1\u003etensorrt_llm\u003c/c1\u003e, \u003cc1\u003ellama_cpp\u003c/c1\u003e, \u003cc1\u003eollama\u003c/c1\u003e, \u003cc1\u003ehf_transformers\u003c/c1\u003e or \u003cc1\u003egeneric\u003c/c1\u003e. Default is \u003cc1\u003eauto\u003c/c1\u003e, which picks \u003cc1\u003ellama_cpp\u003c/c1\u003e for GGUF quantizations and \u003cc1\u003evllm\u003c/c1\u003e otherwise. \u003cc2\u003egeneric\u003c/c2\u003e reproduces the framework-agnostic estimate of earlier API versions (\u003cc2\u003elegacy_auto\u003c/c2\u003e is accepted as an alias). The response's \u003cc1\u003eframework_preset\u003c/c1\u003e field names the runtime used.\",\"frameworkPresetFinetuning\":\"Training framework the estimate models: \u003cc1\u003eauto\u003c/c1\u003e, \u003cc1\u003ehf_trainer\u003c/c1\u003e, \u003cc1\u003eunsloth\u003c/c1\u003e, \u003cc1\u003etorchtune\u003c/c1\u003e or \u003cc1\u003egeneric\u003c/c1\u003e. Default is \u003cc1\u003eauto\u003c/c1\u003e, which picks \u003cc1\u003ehf_trainer\u003c/c1\u003e. \u003cc2\u003egeneric\u003c/c2\u003e reproduces the framework-agnostic estimate of earlier API versions (\u003cc2\u003elegacy_auto\u003c/c2\u003e is accepted as an alias). The response's \u003cc1\u003eframework_preset\u003c/c1\u003e field names the framework used.\"},\"examples\":{\"curlInference\":\"cURL Example (Inference)\",\"pythonInference\":\"Python Example (Inference)\",\"curlFinetuning\":\"cURL Example (Fine-tuning)\",\"pythonFinetuning\":\"Python Example (Fine-tuning)\",\"shellRequest\":\"Shell Request\",\"pythonScript\":\"Python Script\",\"responseInference\":\"Response Body (Inference Example)\",\"inferenceOutput\":\"Inference Output\",\"responseFinetuning\":\"Response Body (Fine-tuning Example)\",\"finetuningOutput\":\"Fine-tuning Output\"},\"enumOptions\":{\"title\":\"Memory Status Enum Options\",\"description\":\"The \u003cc1\u003ememory_status\u003c/c1\u003e field returns one of the following lowercase enum keys:\",\"valueHeader\":\"Enum Value\",\"descHeader\":\"Description\",\"okay\":\"Fits with safe margin representing 50% or less of VRAM\",\"moderate\":\"Fits with moderate usage representing 75% or less of VRAM\",\"high\":\"Tight fit representing 90% or less of VRAM\",\"veryHigh\":\"Very close to capacity, exceeding 90% of VRAM\",\"insufficient\":\"Exceeds available VRAM capacity\",\"error\":\"GPU configuration or sizing calculation error\"},\"routes\":{\"title\":\"Hardware \u0026 Model Listing Routes\",\"gpus\":{\"desc\":\"Retrieve valid gpu_key parameters, hardware models, and VRAM capacities.\",\"jsonResponse\":\"JSON Response\"},\"llms\":{\"desc\":\"Retrieve supported llm_key parameters mapped to model variant configurations. Supports query pagination parameters such as page and limit (e.g., ?page=1\u0026limit=100).\",\"jsonResponse\":\"JSON Response\"}}},\"BlogPostPage\":{\"breadcrumbs\":{\"allPosts\":\"All Posts\"},\"meta\":{\"by\":\"By\",\"on\":\"on\"},\"badges\":{\"guestAuthor\":\"Guest Author\"},\"citation\":{\"needToCite\":\"Need to cite this page? View citation guide\",\"citationInfo\":\"To cite this post, please use the following information:\",\"author\":\"Author:\",\"pageTitle\":\"Page Title:\",\"websiteName\":\"Website Name:\",\"publicationDate\":\"Publication Date:\",\"lastUpdated\":\"Last Updated:\",\"url\":\"URL:\",\"mlaExample\":\"MLA (9th edition) Example:\",\"authorName\":\"Thor, Wei Ming\",\"websiteNameValue\":\"ApX Machine Learning\"},\"banner\":{\"needGpuCheck\":\"Need to check if your GPU can run an LLM?\",\"vramCalculatorDesc\":\"Use the VRAM Calculator to estimate memory requirements for different models\",\"calculateNow\":\"Calculate Now\"},\"topCodingLLMs\":{\"title\":\"Top Local LLMs for Coding\",\"subtitle\":\"Based on aggregated coding performance\",\"viewFullList\":\"View full list\"},\"llmDirectory\":{\"title\":\"Find the Best LLM for Your GPU\",\"subtitle\":\"Explore the LLM directory to find models compatible with your hardware.\",\"exploreCta\":\"Explore LLM Directory\",\"or\":\"or\",\"vramCalculatorDesc\":\"Calculate the VRAM requirements of an LLM.\",\"vramCalculatorCta\":\"VRAM Calculator\"},\"recommendedCourses\":{\"title\":\"Recommended Courses\",\"subtitle\":\"Related to this post\",\"viewAllCourses\":\"View all courses\"}
16,\"recommendedPosts\":{\"title\":\"Recommended Posts\"},\"connectWithUs\":{\"title\":\"Connect With Us\",\"subtitle\":\"Follow for updates on AI/ML research and practical tips.\",\"linkedin\":\"LinkedIn\",\"github\":\"GitHub\",\"sponsorContent\":\"Sponsor Content\"},\"copyright\":\"© {year} ApX Machine Learning. All rights reserved.\"},\"PostList\":{\"latestPosts\":\"Latest Posts\",\"by\":\"By\",\"on\":\"on\",\"connectWithUs\":\"Connect With Us\",\"followForUpdates\":\"Follow for updates on AI/ML research and practical tips.\",\"linkedin\":\"LinkedIn\",\"sponsorContent\":\"Sponsor Content\"},\"Hardware\":{\"directoryTitle\":\"LLM Hardware Directory\",\"directorySubtitle\":\"Local LLM Performance, Memory Limits \u0026 Compatibility\",\"directoryDescription\":\"Find out exactly which open-weights LLMs fit on your hardware. Compare Apple Silicon, NVIDIA GeForce, workstation, and datacenter GPUs with ground-truth VRAM calculations and generation speeds.\",\"searchLabel\":\"Search\",\"searchPlaceholder\":\"Search hardware by name or chip (e.g. M4, RTX 4090)...\",\"allArchitectures\":\"All\",\"appleSilicon\":\"Apple Silicon\",\"nvidia\":\"NVIDIA\",\"amd\":\"AMD\",\"intel\":\"Intel\",\"datacenter\":\"Datacenter\",\"canRunModel\":\"Can Run Model\",\"allModels\":\"All Models\",\"selectModelPlaceholder\":\"Select a model...\",\"precision\":\"Precision\",\"minVramRequired\":\"Requires ~{vram} GB usable VRAM\",\"memory\":\"Memory\",\"bandwidth\":\"Bandwidth\",\"tdp\":\"TDP\",\"usableMemory\":\"Usable VRAM\",\"viewHardware\":\"View Compatibility\",\"totalGpus\":\"{count} Hardware Configurations\",\"noResults\":\"No hardware matches your search criteria.\",\"ctaTestCalculator\":\"Open in Calculator\",\"ctaCustomTest\":\"Test in Calculator\",\"heroSpecs\":\"Hardware Specifications\",\"usableMemoryLimit\":\"Usable Memory Ceiling\",\"maxModelClass\":\"Max Model Class\",\"modelClass70B\":\"70B+ Models\",\"modelClass70BExact\":\"70B Models\",\"modelClass32B\":\"32B Models\",\"modelClass14B\":\"14B Models\",\"modelClass7B8B\":\"7B-8B Models\",\"modelClass8B\":\"8B Models\",\"modelClass3B\":\"3B Models\",\"modelClass1B\":\"1B Models\",\"modelClassLessThan3B\":\"\u003c 3B Models\",\"modelClassLessThan1B\":\"\u003c 1B Models\",\"editorialSummaryApple\":\"The {name} uses a unified memory architecture. For reliable local LLM inference without OS memory pressure, this benchmark models a {usableBudget}% usable allocation ({usableMemory} GB) with {bandwidth} GB/s unified memory bandwidth.\",\"editorialSummaryNvidia\":\"The {name} features {memory} GB of dedicated VRAM and {bandwidth} GB/s memory bandwidth, supporting CUDA and TensorRT-LLM runtimes for full-precision and quantized inference.\",\"editorialSummaryGeneric\":\"The {name} provides {memory} GB of memory with {bandwidth} GB/s bandwidth, supporting local inference via ROCm and Vulkan acceleration.\",\"contextCurveTitle\":\"VRAM Scaling by Context Length\",\"contextCurveDescription\":\"Estimated memory requirements as context length increases. Curves crossing above the reference line indicate Out of Memory (OOM).\",\"speedCurveTitle\":\"Generation Speed Comparison\",\"speedCurveDescription\":\"Estimated token generation throughput on {name}.\",\"viewContext\":\"Context\",\"viewSpeed\":\"Speed\",\"tokensPerSecond\":\"Tokens / Sec\",\"contextLength\":\"Context Length\",\"memoryRequiredGb\":\"Memory Required (GB)\",\"usableLimit\":\"Usable Limit ({limit} GB)\",\"compatibilityTitle\":\"Best Models to Run on {name}\",\"compatibilityDescription\":\"Inference throughput, memory compatibility, and generation speeds for open-weights LLMs on {name}.\",\"sortBy\":\"Sort by\",\"sortByTps\":\"Highest TPS\",\"sortByRank\":\"Rank\",\"sortByCodingRank\":\"Coding Rank\",\"sortByParams\":\"Model Size\",\"tableRank\":\"Rank\",\"tableCodingRank\":\"Coding Rank\",\"runnableOnly\":\"Runnable only\",\"tableModel\":\"Model\",\"tableParams\":\"Parameters\",\"tableQuant\":\"Precision\",\"tableSpeed\":\"TPS (Approx.)\",\"tableTtft\":\"TTFT\",\"tableStatus\":\"Can Run\",\"statusOptimal\":\"Optimal\",\"statusComfortable\":\"Comfortable\",\"statusUpperBound\":\"Upper Bound\",\"statusOom\":\"Out of Memory\",\"fineTuningTitle\":\"Fine-Tuning Capacity\",\"fineTuningDescription\":\"Hardware capacity for local training and adapter fine-tuning.\",\"fullFineTune\":\"Full Parameter Training\",\"loraTraining\":\"LoRA (16-bit)\",\"qloraTraining\":\"QLoRA (4-bit)\",\"presetsTitle\":\"Interactive
16Calculator Presets\",\"presetsDescription\":\"Load this hardware into the VRAM Calculator with pre-configured parameters:\",\"filters\":\"Filters\",\"resetFilters\":\"Reset Filters\",\"minUsableVram\":\"Minimum Usable Memory\",\"anyVram\":\"Any\",\"sortByMemory\":\"Memory (High to Low)\",\"sortByBandwidth\":\"Bandwidth\",\"sortByName\":\"Name\",\"tableView\":\"Table\",\"gridView\":\"Cards\",\"actions\":\"Actions\",\"calculator\":\"Calculator\",\"allHardware\":\"All Hardware\",\"consumer\":\"Consumer\",\"datacenterWorkstation\":\"Datacenter\",\"usable\":\"usable\",\"type\":\"Type\",\"discrete\":\"Discrete\",\"unified\":\"Unified\",\"contextLengthFilter\":\"Context\",\"kvCachePrecision\":\"KV Cache\",\"gpuCount\":\"GPUs\",\"chartPrecision\":\"Precision\",\"chartModels\":\"Compare Models\",\"chartModelsPlaceholder\":\"Select models (max 4)\",\"fineTuningContext\":\"Context Length\",\"advancedCalculatorNote\":\"Need custom layer dimensions, offloading configurations, or distributed multi-node topology?\",\"openInAdvancedCalculator\":\"Open in Advanced VRAM Calculator\",\"interactiveSimulationNote\":\"Recalculated in real time via WebAssembly\",\"configureCalculator\":\"Open in Calculator\",\"maxInferenceClass\":\"Max Inference Class\",\"inferenceFeasibilityTitle\":\"Inference Capacity\",\"inferenceFeasibilityDescription\":\"Maximum model parameter class runnable by weight precision.\",\"fourBitPrecision\":\"4-bit\",\"eightBitPrecision\":\"8-bit\",\"sixteenBitPrecision\":\"16-bit\",\"unrankedDivider\":\"Unranked Models (Sorted by Speed)\",\"faqTitle\":\"Frequently Asked Questions\",\"faqBestModelsTitle\":\"What are the best models to run on {name}?\",\"faqBestModelsIntro\":\"Based on model rankings and {usableMemory} GB usable VRAM:\",\"faqBestOverall\":\"Top Overall\",\"faqBestCoding\":\"Top Coding\",\"faqBestBalanced\":\"Optimal Daily Driver\",\"faqBestMaxCapacity\":\"Maximum Model Size\",\"faqHowCalculatedTitle\":\"How are VRAM, throughput, and latency calculated?\",\"faqHowCalculatedIntro\":\"Calculations are determined using \u003clink\u003eapxml.com\u003c/link\u003e modeling, combining LLM architecture analysis with GPU hardware physics.\",\"faqHowCalculatedVramTitle\":\"VRAM and Context Scaling\",\"faqHowCalculatedVram\":\"Memory requirements are determined from model architecture, including parameter count, layer configuration, and attention structure. The KV cache footprint scales deterministically based on context length, batch size, and cache precision.\",\"faqHowCalculatedTpsTitle\":\"Throughput (TPS)\",\"faqHowCalculatedTps\":\"Token generation is calculated in the memory-bandwidth-bound decode phase, measuring the time required to stream model weights and KV cache relative to hardware memory bandwidth.\",\"faqHowCalculatedTtftTitle\":\"Latency (TTFT)\",\"faqHowCalculatedTtft\":\"Time to first token is calculated from the compute operations required during prompt prefill relative to hardware tensor compute capacity.\",\"architecture\":\"Architecture\",\"breadcrumbHome\":\"Home\",\"breadcrumbHardware\":\"Hardware\",\"usableBudgetApple\":\"({percent}% usable budget)\",\"aggregateBandwidth\":\"(aggregate)\",\"metaPageTitle\":\"Best Models to Run on {name} ({month} {year})\",\"metaPageDescription\":\"Discover the best open-weights LLMs to run on {name}. Usable VRAM limits ({usableMemory} GB), estimated tokens/sec, and context scaling curves.\",\"workloadRecommendationsTitle\":\"Workload Recommendations\",\"workloadRecommendationsDescription\":\"Guidance for optimal precision and operational ceilings on {name}.\",\"inferenceSweetSpotTitle\":\"Inference Sweet Spot\",\"inferenceSweetSpot70B\":\"Suitable for 70B parameter models at Q4/Q8 with extended 32k context windows, or high-throughput batching of smaller dense architectures.\",\"inferenceSweetSpot32B\":\"Optimized for 14B models at uncompressed or Q8 precision, and 32B models at Q4 with up to 32k context.\",\"inferenceSweetSpot8B\":\"Optimal for 8B models across extended context (32k+). 14B models fit comfortably at Q4 precision with standard context.\",\"inferenceSweetSpotLight\":\"Recommended for lightweight models (1B to 3B) at FP16/Q8, and 7B/8B models at aggressive 4-bit quantizations.\",\"bandwidthSpeedProfileTitle\":\"Bandwidth \u0026 Speed Profile\",\"bandwidthSpeedProfileText\":\"With {bandwidth} GB/s aggregate bandwidth, batch size 1 inference operates in a memory-bandwidth boun
16d regime. Generates approximately {tps8b} tok/s on an 8B Q4 model and {tps70b} tok/s on a 70B Q4 model.\",\"unifiedMemoryBudgetTitle\":\"Unified Memory Budget\",\"unifiedMemoryBudgetText\":\"Modeled with a 75% usable allocation budget to preserve system RAM for macOS and display compositor processes.\",\"faqAccuracyTitle\":\"How accurate are these estimates?\",\"faqAccuracyIntro\":\"Calculations are calibrated against empirical ground-truth benchmark runs across diverse architectures and hardware. Because the underlying mechanics are benchmark-calibrated, estimates remain accurate across different configurations without needing to run physical tests for every scenario.\",\"faqAccuracyVramTitle\":\"VRAM Accuracy\",\"faqAccuracyVram\":\"Memory estimates are deterministic and closely match actual allocation. Minor differences typically stay within 3% to 5% due to CUDA runtime overhead or memory allocator fragmentation.\",\"faqAccuracySpeedTitle\":\"Speed and Latency Accuracy\",\"faqAccuracySpeed\":\"Throughput and TTFT typically vary within 5% to 10% depending on the specific inference runtime, kernel implementations, and system power limits.\"}}
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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.